This document describes Visinets, a web-based software tool for pathway modeling and dynamic visualization. The tool uses causal mapping (CMAP) as its mathematical approach, which graphically represents biological networks and interactions. The authors tested Visinets by: 1) building an executable EGFR-MAPK pathway model using its graphical modeling interface; and 2) translating an existing ODE-based insulin signaling model into CMAP format. The testing confirmed CMAP's potential for broad pathway modeling and visualization applications. Visinets offers pathway analysis and dynamic simulation in real time through its web-based graphical interface, providing an alternative for biomedical research.
IRJET - Prediction of Risk Factor of the Patient with Hepatocellular Carcinom...IRJET Journal
This document discusses using machine learning to predict the risk factor of patients with hepatocellular carcinoma (HCC or liver cancer) based on medical test results. It involves collecting patient data, preprocessing the data, feature selection to identify key predictive features, and using machine learning algorithms like support vector machines (SVM) and random forests. The best model achieved 95% accuracy using SVM with 5 selected features to classify patients as high or low risk, where high risk means less than one year lifetime. The system could help predict survival time and guide treatment decisions for liver cancer patients.
This document introduces BioPreDyn-bench, a suite of benchmark problems for dynamic modelling in systems biology. The suite contains 6 benchmark problems ranging from medium to large-scale kinetic models of organisms such as E. coli, S. cerevisiae, D. melanogaster, and human cells. For each benchmark, the document provides a description, implementations in various formats, computational results from specific solvers, and analysis. The suite aims to serve as reference test cases to evaluate and compare parameter estimation methods for dynamic models in systems biology.
IRJET- Analysis of Vehicle Number Plate RecognitionIRJET Journal
This document summarizes research on techniques for enhancing license plate images and improving vehicle number plate recognition. It discusses using forward motion deblurring, scale-based region growing, blur estimation, blind image deblurring, and a no-reference metric to enhance license plate image details and obtain deblurred images. The document also reviews related work applying these and other techniques, such as binary SIFT, energy cooperation in wireless networks, and parametric blur estimation for natural image restoration. The goal is to develop more accurate and robust methods for automatic license plate recognition.
AN EFFICIENT PSO BASED ENSEMBLE CLASSIFICATION MODEL ON HIGH DIMENSIONAL DATA...ijsc
As the size of the biomedical databases are growing day by day, finding an essential features in the disease prediction have become more complex due to high dimensionality and sparsity problems. Also, due to the
availability of a large number of micro-array datasets in the biomedical repositories, it is difficult to analyze, predict and interpret the feature information using the traditional feature selection based classification models. Most of the traditional feature selection based classification algorithms have computational issues such as dimension reduction, uncertainty and class imbalance on microarray datasets. Ensemble classifier is one of the scalable models for extreme learning machine due to its high efficiency, the fast processing speed for real-time applications. The main objective of the feature selection
based ensemble learning models is to classify the high dimensional data with high computational efficiency
and high true positive rate on high dimensional datasets. In this proposed model an optimized Particle swarm optimization (PSO) based Ensemble classification model was developed on high dimensional microarray
datasets. Experimental results proved that the proposed model has high computational efficiency compared to the traditional feature selection based classification models in terms of accuracy , true positive rate and error rate are concerned.
IRJET - Breast Cancer Risk and Diagnostics using Artificial Neural Network(ANN)IRJET Journal
This document describes research using an artificial neural network (ANN) to classify breast cancer as benign or malignant based on the Wisconsin Breast Cancer dataset. The ANN model was trained and tested on 683 instances from the dataset. The model achieved 97.8% accuracy on the training set and 97.5% accuracy on the test set. Various performance metrics including mean absolute error, root mean square error, and kappa statistics were used to evaluate the model, demonstrating low error rates. The ANN model outperformed other classification algorithms in related work and efficiently classified breast cancer with high accuracy and precision.
An approach for breast cancer diagnosis classification using neural networkacijjournal
Artificial neural network has been widely used in various fields as an intelligent tool in recent years, such
as artificial intelligence, pattern recognition, medical diagnosis, machine learning and so on. The
classification of breast cancer is a medical application that poses a great challenge for researchers and
scientists. Recently, the neural network has become a popular tool in the classification of cancer datasets.
Classification is one of the most active research and application areas of neural networks. Major
disadvantages of artificial neural network (ANN) classifier are due to its sluggish convergence and always
being trapped at the local minima. To overcome this problem, differential evolution algorithm (DE) has
been used to determine optimal value or near optimal value for ANN parameters. DE has been applied
successfully to improve ANN learning from previous studies. However, there are still some issues on DE
approach such as longer training time and lower classification accuracy. To overcome these problems,
island based model has been proposed in this system. The aim of our study is to propose an approach for
breast cancer distinguishing between different classes of breast cancer. This approach is based on the
Wisconsin Diagnostic and Prognostic Breast Cancer and the classification of different types of breast
cancer datasets. The proposed system implements the island-based training method to be better accuracy
and less training time by using and analysing between two different migration topologies
Disease Identification and Detection in Apple Treeijtsrd
Apple trees are widely used in the landscaping of vast farms and private gardens. Also, the kings eye finds it difficult to detect disease early and prevent it from spreading to other parts of the plant. Distinguishing and obtaining accuracy, deep models relating to the convolutional neural network were developed. This text compares and compares various current models. It includes research that can be applied to differentiate and differentiate plant leaf infections. R Tanseer Ahmed | Dr. S.K Manju Bargavi "Disease Identification and Detection in Apple Tree" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd42405.pdf Paper URL: https://www.ijtsrd.comcomputer-science/other/42405/disease-identification-and-detection-in-apple-tree/r-tanseer-ahmed
The document evaluates the bag of features technique for visual object detection. It compares the performance of support vector machines, k-nearest neighbors, and decision trees on 6 object classes using SURF keypoints and SIFT descriptors. SVM achieved the best accuracy rate of 91.9% while decision trees performed worst at 71.1%. The author proposes two enhancements: 1) a hybrid algorithm combining bag of features with geometric constraints or 2) reimplementing the algorithm with a convolutional neural network to incorporate spatial information.
IRJET - Prediction of Risk Factor of the Patient with Hepatocellular Carcinom...IRJET Journal
This document discusses using machine learning to predict the risk factor of patients with hepatocellular carcinoma (HCC or liver cancer) based on medical test results. It involves collecting patient data, preprocessing the data, feature selection to identify key predictive features, and using machine learning algorithms like support vector machines (SVM) and random forests. The best model achieved 95% accuracy using SVM with 5 selected features to classify patients as high or low risk, where high risk means less than one year lifetime. The system could help predict survival time and guide treatment decisions for liver cancer patients.
This document introduces BioPreDyn-bench, a suite of benchmark problems for dynamic modelling in systems biology. The suite contains 6 benchmark problems ranging from medium to large-scale kinetic models of organisms such as E. coli, S. cerevisiae, D. melanogaster, and human cells. For each benchmark, the document provides a description, implementations in various formats, computational results from specific solvers, and analysis. The suite aims to serve as reference test cases to evaluate and compare parameter estimation methods for dynamic models in systems biology.
IRJET- Analysis of Vehicle Number Plate RecognitionIRJET Journal
This document summarizes research on techniques for enhancing license plate images and improving vehicle number plate recognition. It discusses using forward motion deblurring, scale-based region growing, blur estimation, blind image deblurring, and a no-reference metric to enhance license plate image details and obtain deblurred images. The document also reviews related work applying these and other techniques, such as binary SIFT, energy cooperation in wireless networks, and parametric blur estimation for natural image restoration. The goal is to develop more accurate and robust methods for automatic license plate recognition.
AN EFFICIENT PSO BASED ENSEMBLE CLASSIFICATION MODEL ON HIGH DIMENSIONAL DATA...ijsc
As the size of the biomedical databases are growing day by day, finding an essential features in the disease prediction have become more complex due to high dimensionality and sparsity problems. Also, due to the
availability of a large number of micro-array datasets in the biomedical repositories, it is difficult to analyze, predict and interpret the feature information using the traditional feature selection based classification models. Most of the traditional feature selection based classification algorithms have computational issues such as dimension reduction, uncertainty and class imbalance on microarray datasets. Ensemble classifier is one of the scalable models for extreme learning machine due to its high efficiency, the fast processing speed for real-time applications. The main objective of the feature selection
based ensemble learning models is to classify the high dimensional data with high computational efficiency
and high true positive rate on high dimensional datasets. In this proposed model an optimized Particle swarm optimization (PSO) based Ensemble classification model was developed on high dimensional microarray
datasets. Experimental results proved that the proposed model has high computational efficiency compared to the traditional feature selection based classification models in terms of accuracy , true positive rate and error rate are concerned.
IRJET - Breast Cancer Risk and Diagnostics using Artificial Neural Network(ANN)IRJET Journal
This document describes research using an artificial neural network (ANN) to classify breast cancer as benign or malignant based on the Wisconsin Breast Cancer dataset. The ANN model was trained and tested on 683 instances from the dataset. The model achieved 97.8% accuracy on the training set and 97.5% accuracy on the test set. Various performance metrics including mean absolute error, root mean square error, and kappa statistics were used to evaluate the model, demonstrating low error rates. The ANN model outperformed other classification algorithms in related work and efficiently classified breast cancer with high accuracy and precision.
An approach for breast cancer diagnosis classification using neural networkacijjournal
Artificial neural network has been widely used in various fields as an intelligent tool in recent years, such
as artificial intelligence, pattern recognition, medical diagnosis, machine learning and so on. The
classification of breast cancer is a medical application that poses a great challenge for researchers and
scientists. Recently, the neural network has become a popular tool in the classification of cancer datasets.
Classification is one of the most active research and application areas of neural networks. Major
disadvantages of artificial neural network (ANN) classifier are due to its sluggish convergence and always
being trapped at the local minima. To overcome this problem, differential evolution algorithm (DE) has
been used to determine optimal value or near optimal value for ANN parameters. DE has been applied
successfully to improve ANN learning from previous studies. However, there are still some issues on DE
approach such as longer training time and lower classification accuracy. To overcome these problems,
island based model has been proposed in this system. The aim of our study is to propose an approach for
breast cancer distinguishing between different classes of breast cancer. This approach is based on the
Wisconsin Diagnostic and Prognostic Breast Cancer and the classification of different types of breast
cancer datasets. The proposed system implements the island-based training method to be better accuracy
and less training time by using and analysing between two different migration topologies
Disease Identification and Detection in Apple Treeijtsrd
Apple trees are widely used in the landscaping of vast farms and private gardens. Also, the kings eye finds it difficult to detect disease early and prevent it from spreading to other parts of the plant. Distinguishing and obtaining accuracy, deep models relating to the convolutional neural network were developed. This text compares and compares various current models. It includes research that can be applied to differentiate and differentiate plant leaf infections. R Tanseer Ahmed | Dr. S.K Manju Bargavi "Disease Identification and Detection in Apple Tree" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd42405.pdf Paper URL: https://www.ijtsrd.comcomputer-science/other/42405/disease-identification-and-detection-in-apple-tree/r-tanseer-ahmed
The document evaluates the bag of features technique for visual object detection. It compares the performance of support vector machines, k-nearest neighbors, and decision trees on 6 object classes using SURF keypoints and SIFT descriptors. SVM achieved the best accuracy rate of 91.9% while decision trees performed worst at 71.1%. The author proposes two enhancements: 1) a hybrid algorithm combining bag of features with geometric constraints or 2) reimplementing the algorithm with a convolutional neural network to incorporate spatial information.
IRJET- Student Placement Prediction using Machine LearningIRJET Journal
This document describes a study that uses machine learning algorithms to predict whether students will be placed in jobs after graduating. Specifically, it uses Naive Bayes and K-Nearest Neighbors classifiers to analyze historical student data and predict placements. The algorithms consider parameters like academic results, skills, and previous placement data to make predictions. This system aims to help institutions increase placement percentages by identifying students' strengths and areas for improvement. It is intended to benefit both students in preparing for careers and placement cells in targeting support. Accurately predicting placements could boost a school's reputation by demonstrating career outcomes.
Prognosticating Autism Spectrum Disorder Using Artificial Neural Network: Lev...Avishek Choudhury
Autism spectrum condition (ASC) or autism spectrum disorder (ASD) is primarily identified with the help of behavioral indications encompassing social, sensory and motor characteristics. Although categorized, recurring motor actions are measured during diagnosis, quantifiable measures that ascertain kinematic physiognomies in the movement configurations of autistic persons are not adequately studied, hindering the advances in understanding the etiology of motor mutilation. Subject aspects such as behavioral characters that influences ASD need further exploration. Presently, limited autism datasets concomitant with screening ASD are available, and a majority of them are genetic. Hence, in this study, we used a dataset related to autism screening enveloping ten behavioral and ten personal attributes that have been effective in diagnosing ASD cases from controls in behavior science. ASD diagnosis is time exhaustive and uneconomical. The burgeoning ASD cases worldwide mandate a need for the fast and economical screening tool. Our study aimed to implement an artificial neural network with the Levenberg- Marquardt algorithm to detect ASD and examine its predictive accuracy. Consecutively, develop a clinical decision support system for early ASD identification.
Machine Learning in Modern Medicine with Erin LeDell at Stanford MedSri Ambati
Machine learning and AI company H2O.ai presented on machine learning applications in modern medicine. They discussed how electronic health records, genomics, wearables, and other data sources can be used with machine learning for personalized healthcare, disease prediction and prevention. H2O's software platform allows building models at scale from large datasets using algorithms like random forests, deep learning and ensembles. Demonstrations showed predicting HIV treatment failure and classifying breast cancer malignancy from medical images, achieving high accuracy. H2O aims to make machine learning accessible and scalable for improving medical research and care.
MOVIE SUCCESS PREDICTION AND PERFORMANCE COMPARISON USING VARIOUS STATISTICAL...ijaia
Movies are among the most prominent contributors to the global entertainment industry today, and they
are among the biggest revenue-generating industries from a commercial standpoint. It's vital to divide
films into two categories: successful and unsuccessful. To categorize the movies in this research, a variety
of models were utilized, including regression models such as Simple Linear, Multiple Linear, and Logistic
Regression, clustering techniques such as SVM and K-Means, Time Series Analysis, and an Artificial
Neural Network. The models stated above were compared on a variety of factors, including their accuracy
on the training and validation datasets as well as the testing dataset, the availability of new movie
characteristics, and a variety of other statistical metrics. During the course of this study, it was discovered
that certain characteristics have a greater impact on the likelihood of a film's success than others. For
example, the existence of the genre action may have a significant impact on the forecasts, although another
genre, such as sport, may not. The testing dataset for the models and classifiers has been taken from the
IMDb website for the year 2020. The Artificial Neural Network, with an accuracy of 86 percent, is the best
performing model of all the models discussed.
A NOVEL APPROACH TO ERROR DETECTION AND CORRECTION OF C PROGRAMS USING MACHIN...IJCI JOURNAL
There has always been a struggle for programmers to identify the errors while executing a program- be it
syntactical or logical error. This struggle has led to a research in identification of syntactical and logical
errors. This paper makes an attempt to survey those research works which can be used to identify errors as
well as proposes a new model based on machine learning and data mining which can detect logical and
syntactical errors by correcting them or providing suggestions. The proposed work is based on use of
hashtags to identify each correct program uniquely and this in turn can be compared with the logically
incorrect program in order to identify errors.
1) The document describes a final semester project analyzing agricultural sector data using hybrid algorithms and machine learning techniques.
2) It involves collecting cost and capital logs, applying algorithms like genetic, fuzzy logic, and neural networks to generate mean cost values and predict commodity prices.
3) Validation techniques like internal and external clustering are used to improve the analysis and resulting prediction, which is subject to change with new data but provides an accurate forecast.
This document describes a project to predict breast cancer outcomes using machine learning. It will compare algorithms like SVM, logistic regression, random forest and KNN on different datasets. The project aims to develop a technique with minimum error to accurately predict diagnosis, treatment and outcomes. It will be implemented in a simulation environment in Jupyter platform. The objectives are to identify helpful features for predicting cancer type and create a more accurate prediction model than doctors.
The International Journal of Engineering and Science (The IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
This document describes a proposed system to detect plant diseases using machine learning and provide remedial measures. It will use a mobile app to classify plant leaf images using a TensorFlow Lite model trained with InceptionV3. The model will identify the disease and fetch details like treatment from a database to display to the user. This aims to make plant disease detection and treatment advice more easily accessible compared to existing computer-based systems.
This document provides a summary and details of Madhavi Tippani's experience and qualifications. She has over 5 years of experience in biomedical engineering, with skills in programming, data analysis, and medical imaging. Currently she is a Research Programmer analyzing corneal imaging data and studying corneal regeneration. Her experience also includes projects involving image and signal processing, biomedical devices, and statistical analysis.
IRJET- A Comparative Research of Rule based Classification on Dataset using W...IRJET Journal
This document discusses and compares the performance of four rule-based classification algorithms (Decision Table, One R, PART, and Zero R) on different datasets using the WEKA data mining tool. It first provides background on classification and rule-based classification in data mining. It then describes the four algorithms and the experimental process used to implement them in WEKA, evaluate their performance based on accuracy, number of correct/incorrect predictions, and execution time, and analyze the results.
The International Journal of Engineering and Science (The IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
Simplified Knowledge Prediction: Application of Machine Learning in Real LifePeea Bal Chakraborty
Machine learning is the scientific study of algorithms and statistical models that is used by the machines to perform a specific task depending on patterns and inference rather than explicit instructions. This research and analysis aims to observe how precisely a machine can predict that a patient suspected of breast cancer is having malignant or benign cancer.In this paper the classification of cancer type and prediction of risk levels is done by various model of machine learning and is pictorially depicted by various tools of visual analytics.
Hybrid prediction model with missing value imputation for medical data 2015-g...Jitender Grover
The document presents a novel hybrid prediction model called HPM-MI that uses K-means clustering and multilayer perceptron (MLP) to improve predictive classification for medical data with missing values. The model first analyzes 11 different imputation techniques using K-means clustering to select the best one for filling missing values in the data. It then uses K-means clustering again to validate class labels and remove incorrectly classified instances before applying the MLP classifier. The model is tested on three medical datasets from the UCI repository and shows improved accuracy, sensitivity, specificity and other metrics compared to existing methods, particularly when datasets have large numbers of missing values.
RELIABILITY BASED POWER DISTRIBUTION SYSTEMS PLANNING USING CREDIBILITY THEORYpaperpublications3
Abstract: This paper presents an analytical technique for distribution system planning based on reliability evaluation using credibility theory. With the development of economy and society power planning is facing with the influence of much uncertainty, which are mainly power distribution network. Power system especially at the distribution level is prone to failures and disturbances as many devices are responsible for the successful operation of a radial distribution system. We also mention whether the work has been done at the strategic level, i.e. if it concerns the planning of power distribution system based on reliability and uncertainty.
بعض (وليس الكل) ملخصات الأبحاث الجيدة المنشورة فى بعض المجلات الجيدة وفيها تنوع من الافكار الابحاث الابتكارية التى يخدم فيها علوم الحاسبات فيها - انها تطبيقات حياتية
IRJET- Sketch-Verse: Sketch Image Inversion using DCNNIRJET Journal
The document describes a system that uses deep convolutional neural networks to convert face sketch images to photorealistic images. It first constructs a semi-simulated dataset from a large dataset containing face sketches and corresponding photos. It then trains a model using techniques like deep residual learning and perceptual losses. The trained model is able to take face sketches as input and generate photorealistic images as output. An evaluation of the system found a conversion rate of around 70% for test images. The authors aim to improve the model's robustness through additional data augmentation and training.
This document describes a disease prediction system that uses machine learning algorithms like decision trees, random forests and naive Bayes to predict a disease based on symptoms provided by a patient. The researchers developed a logistic regression model to take in symptoms and predict the likely disease. It was created using Python and aims to help busy professionals more easily identify health issues before they become serious. The system was built using techniques like data collection, preprocessing, model training/evaluation and aims to improve performance over iterations. It was found to provide time savings and early disease warnings compared to traditional diagnosis methods.
Efficiency of Prediction Algorithms for Mining Biological DatabasesIOSR Journals
This document analyzes the efficiency of various prediction algorithms for mining biological databases. It discusses prediction through mining biological databases to identify disease risks. It then evaluates several prediction algorithms (ZeroR, OneR, JRip, PART, Decision Table) on a breast cancer dataset using measures like accuracy, sensitivity, specificity, and predictive values. The results show that the JRip and PART algorithms generally had the highest accuracy rates, around 70%, while ZeroR had the lowest accuracy. However, ZeroR had a perfect positive predictive value. The study aims to assess the most efficient algorithms for predictive mining of biological data.
A SURVEY ON BLOOD DISEASE DETECTION USING MACHINE LEARNINGIRJET Journal
This document summarizes a research paper that evaluates different machine learning algorithms for detecting blood diseases from laboratory test results. It first introduces the objective to classify and predict diseases like anemia and leukemia. It then evaluates three algorithms: Gaussian, Random Forest, and Support Vector Classification (SVC). SVC achieved the highest accuracy of 98% for anemia detection. The models are deployed using Streamlit so users can access them online or offline. Benefits include low hardware requirements and mobile access. Future work will add more disease predictions and integrate nutritional guidance.
Proceedings of the 2015 Industrial and Systems Engineering Res.docxwkyra78
Proceedings of the 2015 Industrial and Systems Engineering Research Conference
S. Cetinkaya and J. K. Ryan, eds.
Use of Symbolic Regression for Lean Six Sigma Projects
Daniel Moreno-Sanchez, MSc.
Jacobo Tijerina-Aguilera, MSc.
Universidad de Monterrey
San Pedro Garza Garcia, NL 66238, Mexico
Arlethe Yari Aguilar-Villarreal, MEng.
Universidad Autonoma de Nuevo Leon
San Nicolas de los Garza, NL 66451, Mexico
Abstract
Lean Six Sigma projects and the quality engineering profession have to deal with an extensive selection of tools
most of them requiring specialized training. The increased availability of standard statistical software motivates the
use of advanced data science techniques to identify relationships between potential causes and project metrics. In
these circumstances, Symbolic Regression has received increased attention from researchers and practitioners to
uncover the intrinsic relationships hidden within complex data without requiring specialized training for its
implementation. The objective of this paper is to evaluate the advantages and drawbacks of using computer assisted
Symbolic Regression within the Analyze phase of a Lean Six Sigma project. An application of this approach in a
service industry project is also presented.
Keywords
Symbolic Regression, Data Science, Lean Six Sigma
1. Introduction
Lean Six Sigma (LSS) has become a well-known hybrid methodology for quality and productivity improvement in
organizations. Its wide adoption in several industries has shaped Process Innovation and Operational Excellence
initiatives, enabling LSS to become a main topic in quality practitioner sites of interest [1], recognized Six Sigma
(SS) certification body of knowledge contents [2], and professional society conferences [3].
However LSS projects and the quality engineering profession have to deal with an extensive selection of tools most
of them requiring specialized training. To assist LSS practitioners it is common to categorize tools based on the
traditional DMAIC model which stands for Define, Measure, Analyze, Improve, and Control phases. Table 1
presents an overview of the main tools that are commonly used in each phase of a LSS project, allowing team
members to progressively develop an understanding between realizing each phase’s intent and how the selected
tools can contribute to that purpose.
This paper focuses on the Analyze phase where tools for statistical model building are most likely to be selected.
The increased availability of standard statistical software motivates the use of advanced data science techniques to
identify relationships between potential causes and project metrics. In these circumstances Symbolic Regression
(SR) has received increased attention from researchers and practitioners even though SR is still in an early stage of
commercial availability.
The objective of this paper is to evaluate the advantages and drawbacks o ...
IRJET- Student Placement Prediction using Machine LearningIRJET Journal
This document describes a study that uses machine learning algorithms to predict whether students will be placed in jobs after graduating. Specifically, it uses Naive Bayes and K-Nearest Neighbors classifiers to analyze historical student data and predict placements. The algorithms consider parameters like academic results, skills, and previous placement data to make predictions. This system aims to help institutions increase placement percentages by identifying students' strengths and areas for improvement. It is intended to benefit both students in preparing for careers and placement cells in targeting support. Accurately predicting placements could boost a school's reputation by demonstrating career outcomes.
Prognosticating Autism Spectrum Disorder Using Artificial Neural Network: Lev...Avishek Choudhury
Autism spectrum condition (ASC) or autism spectrum disorder (ASD) is primarily identified with the help of behavioral indications encompassing social, sensory and motor characteristics. Although categorized, recurring motor actions are measured during diagnosis, quantifiable measures that ascertain kinematic physiognomies in the movement configurations of autistic persons are not adequately studied, hindering the advances in understanding the etiology of motor mutilation. Subject aspects such as behavioral characters that influences ASD need further exploration. Presently, limited autism datasets concomitant with screening ASD are available, and a majority of them are genetic. Hence, in this study, we used a dataset related to autism screening enveloping ten behavioral and ten personal attributes that have been effective in diagnosing ASD cases from controls in behavior science. ASD diagnosis is time exhaustive and uneconomical. The burgeoning ASD cases worldwide mandate a need for the fast and economical screening tool. Our study aimed to implement an artificial neural network with the Levenberg- Marquardt algorithm to detect ASD and examine its predictive accuracy. Consecutively, develop a clinical decision support system for early ASD identification.
Machine Learning in Modern Medicine with Erin LeDell at Stanford MedSri Ambati
Machine learning and AI company H2O.ai presented on machine learning applications in modern medicine. They discussed how electronic health records, genomics, wearables, and other data sources can be used with machine learning for personalized healthcare, disease prediction and prevention. H2O's software platform allows building models at scale from large datasets using algorithms like random forests, deep learning and ensembles. Demonstrations showed predicting HIV treatment failure and classifying breast cancer malignancy from medical images, achieving high accuracy. H2O aims to make machine learning accessible and scalable for improving medical research and care.
MOVIE SUCCESS PREDICTION AND PERFORMANCE COMPARISON USING VARIOUS STATISTICAL...ijaia
Movies are among the most prominent contributors to the global entertainment industry today, and they
are among the biggest revenue-generating industries from a commercial standpoint. It's vital to divide
films into two categories: successful and unsuccessful. To categorize the movies in this research, a variety
of models were utilized, including regression models such as Simple Linear, Multiple Linear, and Logistic
Regression, clustering techniques such as SVM and K-Means, Time Series Analysis, and an Artificial
Neural Network. The models stated above were compared on a variety of factors, including their accuracy
on the training and validation datasets as well as the testing dataset, the availability of new movie
characteristics, and a variety of other statistical metrics. During the course of this study, it was discovered
that certain characteristics have a greater impact on the likelihood of a film's success than others. For
example, the existence of the genre action may have a significant impact on the forecasts, although another
genre, such as sport, may not. The testing dataset for the models and classifiers has been taken from the
IMDb website for the year 2020. The Artificial Neural Network, with an accuracy of 86 percent, is the best
performing model of all the models discussed.
A NOVEL APPROACH TO ERROR DETECTION AND CORRECTION OF C PROGRAMS USING MACHIN...IJCI JOURNAL
There has always been a struggle for programmers to identify the errors while executing a program- be it
syntactical or logical error. This struggle has led to a research in identification of syntactical and logical
errors. This paper makes an attempt to survey those research works which can be used to identify errors as
well as proposes a new model based on machine learning and data mining which can detect logical and
syntactical errors by correcting them or providing suggestions. The proposed work is based on use of
hashtags to identify each correct program uniquely and this in turn can be compared with the logically
incorrect program in order to identify errors.
1) The document describes a final semester project analyzing agricultural sector data using hybrid algorithms and machine learning techniques.
2) It involves collecting cost and capital logs, applying algorithms like genetic, fuzzy logic, and neural networks to generate mean cost values and predict commodity prices.
3) Validation techniques like internal and external clustering are used to improve the analysis and resulting prediction, which is subject to change with new data but provides an accurate forecast.
This document describes a project to predict breast cancer outcomes using machine learning. It will compare algorithms like SVM, logistic regression, random forest and KNN on different datasets. The project aims to develop a technique with minimum error to accurately predict diagnosis, treatment and outcomes. It will be implemented in a simulation environment in Jupyter platform. The objectives are to identify helpful features for predicting cancer type and create a more accurate prediction model than doctors.
The International Journal of Engineering and Science (The IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
This document describes a proposed system to detect plant diseases using machine learning and provide remedial measures. It will use a mobile app to classify plant leaf images using a TensorFlow Lite model trained with InceptionV3. The model will identify the disease and fetch details like treatment from a database to display to the user. This aims to make plant disease detection and treatment advice more easily accessible compared to existing computer-based systems.
This document provides a summary and details of Madhavi Tippani's experience and qualifications. She has over 5 years of experience in biomedical engineering, with skills in programming, data analysis, and medical imaging. Currently she is a Research Programmer analyzing corneal imaging data and studying corneal regeneration. Her experience also includes projects involving image and signal processing, biomedical devices, and statistical analysis.
IRJET- A Comparative Research of Rule based Classification on Dataset using W...IRJET Journal
This document discusses and compares the performance of four rule-based classification algorithms (Decision Table, One R, PART, and Zero R) on different datasets using the WEKA data mining tool. It first provides background on classification and rule-based classification in data mining. It then describes the four algorithms and the experimental process used to implement them in WEKA, evaluate their performance based on accuracy, number of correct/incorrect predictions, and execution time, and analyze the results.
The International Journal of Engineering and Science (The IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
Simplified Knowledge Prediction: Application of Machine Learning in Real LifePeea Bal Chakraborty
Machine learning is the scientific study of algorithms and statistical models that is used by the machines to perform a specific task depending on patterns and inference rather than explicit instructions. This research and analysis aims to observe how precisely a machine can predict that a patient suspected of breast cancer is having malignant or benign cancer.In this paper the classification of cancer type and prediction of risk levels is done by various model of machine learning and is pictorially depicted by various tools of visual analytics.
Hybrid prediction model with missing value imputation for medical data 2015-g...Jitender Grover
The document presents a novel hybrid prediction model called HPM-MI that uses K-means clustering and multilayer perceptron (MLP) to improve predictive classification for medical data with missing values. The model first analyzes 11 different imputation techniques using K-means clustering to select the best one for filling missing values in the data. It then uses K-means clustering again to validate class labels and remove incorrectly classified instances before applying the MLP classifier. The model is tested on three medical datasets from the UCI repository and shows improved accuracy, sensitivity, specificity and other metrics compared to existing methods, particularly when datasets have large numbers of missing values.
RELIABILITY BASED POWER DISTRIBUTION SYSTEMS PLANNING USING CREDIBILITY THEORYpaperpublications3
Abstract: This paper presents an analytical technique for distribution system planning based on reliability evaluation using credibility theory. With the development of economy and society power planning is facing with the influence of much uncertainty, which are mainly power distribution network. Power system especially at the distribution level is prone to failures and disturbances as many devices are responsible for the successful operation of a radial distribution system. We also mention whether the work has been done at the strategic level, i.e. if it concerns the planning of power distribution system based on reliability and uncertainty.
بعض (وليس الكل) ملخصات الأبحاث الجيدة المنشورة فى بعض المجلات الجيدة وفيها تنوع من الافكار الابحاث الابتكارية التى يخدم فيها علوم الحاسبات فيها - انها تطبيقات حياتية
IRJET- Sketch-Verse: Sketch Image Inversion using DCNNIRJET Journal
The document describes a system that uses deep convolutional neural networks to convert face sketch images to photorealistic images. It first constructs a semi-simulated dataset from a large dataset containing face sketches and corresponding photos. It then trains a model using techniques like deep residual learning and perceptual losses. The trained model is able to take face sketches as input and generate photorealistic images as output. An evaluation of the system found a conversion rate of around 70% for test images. The authors aim to improve the model's robustness through additional data augmentation and training.
This document describes a disease prediction system that uses machine learning algorithms like decision trees, random forests and naive Bayes to predict a disease based on symptoms provided by a patient. The researchers developed a logistic regression model to take in symptoms and predict the likely disease. It was created using Python and aims to help busy professionals more easily identify health issues before they become serious. The system was built using techniques like data collection, preprocessing, model training/evaluation and aims to improve performance over iterations. It was found to provide time savings and early disease warnings compared to traditional diagnosis methods.
Efficiency of Prediction Algorithms for Mining Biological DatabasesIOSR Journals
This document analyzes the efficiency of various prediction algorithms for mining biological databases. It discusses prediction through mining biological databases to identify disease risks. It then evaluates several prediction algorithms (ZeroR, OneR, JRip, PART, Decision Table) on a breast cancer dataset using measures like accuracy, sensitivity, specificity, and predictive values. The results show that the JRip and PART algorithms generally had the highest accuracy rates, around 70%, while ZeroR had the lowest accuracy. However, ZeroR had a perfect positive predictive value. The study aims to assess the most efficient algorithms for predictive mining of biological data.
A SURVEY ON BLOOD DISEASE DETECTION USING MACHINE LEARNINGIRJET Journal
This document summarizes a research paper that evaluates different machine learning algorithms for detecting blood diseases from laboratory test results. It first introduces the objective to classify and predict diseases like anemia and leukemia. It then evaluates three algorithms: Gaussian, Random Forest, and Support Vector Classification (SVC). SVC achieved the highest accuracy of 98% for anemia detection. The models are deployed using Streamlit so users can access them online or offline. Benefits include low hardware requirements and mobile access. Future work will add more disease predictions and integrate nutritional guidance.
Proceedings of the 2015 Industrial and Systems Engineering Res.docxwkyra78
Proceedings of the 2015 Industrial and Systems Engineering Research Conference
S. Cetinkaya and J. K. Ryan, eds.
Use of Symbolic Regression for Lean Six Sigma Projects
Daniel Moreno-Sanchez, MSc.
Jacobo Tijerina-Aguilera, MSc.
Universidad de Monterrey
San Pedro Garza Garcia, NL 66238, Mexico
Arlethe Yari Aguilar-Villarreal, MEng.
Universidad Autonoma de Nuevo Leon
San Nicolas de los Garza, NL 66451, Mexico
Abstract
Lean Six Sigma projects and the quality engineering profession have to deal with an extensive selection of tools
most of them requiring specialized training. The increased availability of standard statistical software motivates the
use of advanced data science techniques to identify relationships between potential causes and project metrics. In
these circumstances, Symbolic Regression has received increased attention from researchers and practitioners to
uncover the intrinsic relationships hidden within complex data without requiring specialized training for its
implementation. The objective of this paper is to evaluate the advantages and drawbacks of using computer assisted
Symbolic Regression within the Analyze phase of a Lean Six Sigma project. An application of this approach in a
service industry project is also presented.
Keywords
Symbolic Regression, Data Science, Lean Six Sigma
1. Introduction
Lean Six Sigma (LSS) has become a well-known hybrid methodology for quality and productivity improvement in
organizations. Its wide adoption in several industries has shaped Process Innovation and Operational Excellence
initiatives, enabling LSS to become a main topic in quality practitioner sites of interest [1], recognized Six Sigma
(SS) certification body of knowledge contents [2], and professional society conferences [3].
However LSS projects and the quality engineering profession have to deal with an extensive selection of tools most
of them requiring specialized training. To assist LSS practitioners it is common to categorize tools based on the
traditional DMAIC model which stands for Define, Measure, Analyze, Improve, and Control phases. Table 1
presents an overview of the main tools that are commonly used in each phase of a LSS project, allowing team
members to progressively develop an understanding between realizing each phase’s intent and how the selected
tools can contribute to that purpose.
This paper focuses on the Analyze phase where tools for statistical model building are most likely to be selected.
The increased availability of standard statistical software motivates the use of advanced data science techniques to
identify relationships between potential causes and project metrics. In these circumstances Symbolic Regression
(SR) has received increased attention from researchers and practitioners even though SR is still in an early stage of
commercial availability.
The objective of this paper is to evaluate the advantages and drawbacks o ...
EMPIRICAL APPLICATION OF SIMULATED ANNEALING USING OBJECT-ORIENTED METRICS TO...ijcsa
The work is about using Simulated Annealing Algorithm for the effort estimation model parameter
optimization which can lead to the reduction in the difference in actual and estimated effort used in model
development.
The model has been tested using OOP’s dataset, obtained from NASA for research purpose.The data set
based model equation parameters have been found that consists of two independent variables, viz. Lines of
Code (LOC) along with one more attribute as a dependent variable related to software development effort
(DE). The results have been compared with the earlier work done by the author on Artificial Neural
Network (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS) and it has been observed that the
developed SA based model is more capable to provide better estimation of software development effort than
ANN and ANFIS
IRJET - Plant Leaf Disease Diagnosis from Color Imagery using Co-Occurrence M...IRJET Journal
This document presents a method for classifying plant leaf diseases from color images using texture and color features extracted from the images along with an artificial neural network classifier. The proposed system first preprocesses the input images, then extracts color features like mean and standard deviation of HSV color space and texture features like energy, contrast, homogeneity and correlation using a gray level co-occurrence matrix. These features are then used to train a backpropagation neural network classifier to automatically classify test images into disease categories. Experimental results show the backpropagation network provides high accuracy for plant disease classification, with 97.2% accuracy on validation data and lower error rates than support vector machines.
IRJET- Plant Leaf Disease Diagnosis from Color Imagery using Co-Occurrence Ma...IRJET Journal
This document presents a method for classifying plant leaf diseases from color images using texture and color features. The proposed system first preprocesses input images, then extracts features like color (mean, standard deviation of HSV channels) and texture (energy, contrast, homogeneity, correlation from GLCM). These features are used to train a backpropagation neural network classifier. The system was tested on images of six plant diseases and showed minimum training error and good classification accuracy. This automated approach could help inexperienced farmers and experts more accurately diagnose plant diseases.
PREDICT THE QUALITY OF FRESHWATER USING MACHINE LEARNINGIRJET Journal
This document summarizes a research paper that aims to predict water quality using machine learning. It discusses how water quality is an important issue due to contamination negatively impacting human and environmental health. The researchers developed a machine learning model using artificial neural networks and time series analysis to forecast water quality index and categorization. They trained the model on historical water quality data from 2014 in the United States. The study aims to improve current techniques for managing water quality by developing a more effective, reliable and accurate prediction model.
IRJET- Gene Mutation Data using Multiplicative Adaptive Algorithm and Gene On...IRJET Journal
This document presents a methodology for analyzing gene mutation data using ontologies and association rule mining. It aims to develop a common knowledge base for genomic and proteomic analysis by integrating multiple data sources. The methodology involves using k-nearest neighbors algorithm to find similar genes, an iterative multiplicative updating algorithm to solve optimization problems, and SNCoNMF to identify co-regulatory modules between genes, microRNAs and transcription factors. The results are represented using a Bayesian rose tree for efficient visualization of associations between genetic components and diseases.
A simplified predictive framework for cost evaluation to fault assessment usi...IJECEIAES
Software engineering is an integral part of any software development scheme which frequently encounters bugs, errors, and faults. Predictive evaluation of software fault contributes towards mitigating this challenge to a large extent; however, there is no benchmarked framework being reported in this case yet. Therefore, this paper introduces a computational framework of the cost evaluation method to facilitate a better form of predictive assessment of software faults. Based on lines of code, the proposed scheme deploys adopts a machine-learning approach to address the perform predictive analysis of faults. The proposed scheme presents an analytical framework of the correlation-based cost model integrated with multiple standards machine learning (ML) models, e.g., linear regression, support vector regression, and artificial neural networks (ANN). These learning models are executed and trained to predict software faults with higher accuracy. The study considers assessing the outcomes based on error-based performance metrics in detail to determine how well each learning model performs and how accurate it is at learning. It also looked at the factors contributing to the training loss of neural networks. The validation result demonstrates that, compared to logistic regression and support vector regression, neural network achieves a significantly lower error score for software fault prediction.
This document summarizes a project that uses K-means clustering to analyze Twitter data and predict people's reactions to COVID-19 vaccines. The project uses a dataset of COVID-19 vaccine tweets from Kaggle and applies natural language processing and machine learning techniques like preprocessing, sentiment analysis, and unsupervised clustering to classify tweets as expressing positive, negative or neutral sentiment. It then evaluates the model's accuracy in predicting sentiment on test tweet data.
DETECTION OF LIVER INFECTION USING MACHINE LEARNING TECHNIQUESIRJET Journal
This document discusses using machine learning techniques to detect liver infections. It provides an overview of various machine learning methods that have been applied to medical data related to the liver, including supervised learning algorithms like naive Bayes classifiers, k-nearest neighbors, and support vector machines. Deep learning techniques like deep neural networks are also mentioned. The goal is to automatically predict liver diseases early based on complex data from electronic health records, images, genomics and other sources to help doctors and improve patient care and outcomes.
IRJET- Automated Measurement of AVR Feature in Fundus Images using Image ...IRJET Journal
This document summarizes an ongoing project to automatically measure the arteriolar-to-venular ratio (AVR) in fundus images using image processing and machine learning techniques. The project involves six main stages: preprocessing, vessel segmentation, region of interest detection, vessel width measurement, vessel classification into arteries and veins, and AVR calculation. So far, the team has completed the first four stages using image processing in MATLAB and Python. They are now working on the vessel classification stage, evaluating both unsupervised k-means clustering and supervised naive Bayes classification approaches. The goal of the project is to develop a fully automated method without any user input to accurately measure AVR, which is important for predicting cardiovascular and other diseases.
APPLICATION OF SELF-ORGANIZING FEATURE MAPS AND MARKOV CHAINS TO RECOGNITION ...IAEME Publication
The number of studies in the field of behavior analysis is currently experiencing a
significant upsurge. This study presents two cancarantly approaches to identifying
anomalous activity within users’ behavior in cloud infrastructures, each of which
allows researchers to learn from the empirical data collected. The main purpose of
these approaches is the ongoing scoring of users’ actions in cloud infrastructures to
reveal anomalies in their activity. The first approach is based on the technique of
statistical hypothesis testing and uses Kohonen self-organizing maps to generate the
target statistics. The second approach is based on revealing strange activity in the
dynamics of user’s behavior and uses Markov chains to describe their typical actions
IRJET - A Novel Approach for Software Defect Prediction based on Dimensio...IRJET Journal
This document presents a novel approach for software defect prediction using dimensionality reduction techniques. The proposed approach uses an artificial neural network to extract features from initial change measures, and then trains a classifier on the extracted features. This is compared to other dimensionality reduction techniques like principal component analysis, linear discriminant analysis, and kernel principal component analysis. Five open source datasets from NASA are used to evaluate the different techniques based on accuracy, F1 score, and area under the receiver operating characteristic curve. The results show that the artificial neural network approach outperforms the other dimensionality reduction techniques, and kernel principal component analysis performs best among those techniques. The document also discusses related work on using machine learning for software defect prediction.
In the present paper, applicability and
capability of A.I techniques for effort estimation prediction has
been investigated. It is seen that neuro fuzzy models are very
robust, characterized by fast computation, capable of handling
the distorted data. Due to the presence of data non-linearity, it is
an efficient quantitative tool to predict effort estimation. The one
hidden layer network has been developed named as OHLANFIS
using MATLAB simulation environment.
Here the initial parameters of the OHLANFIS are
identified using the subtractive clustering method. Parameters of
the Gaussian membership function are optimally determined
using the hybrid learning algorithm. From the analysis it is seen
that the Effort Estimation prediction model developed using
OHLANFIS technique has been able to perform well over normal
ANFIS Model.
IRJET- Face Recognition of Criminals for Security using Principal Component A...IRJET Journal
This document presents a face recognition system using principal component analysis to identify criminals at airports. The system is trained on images of known criminals collected from law enforcement agencies. It uses PCA for dimensionality reduction to generate eigenfaces from the training images. During testing, it generates an eigenface from the input image and calculates the Euclidean distance between this eigenface and the eigenfaces of the training images. It identifies the criminal as the one corresponding to the training image with the minimum distance, alerting authorities. The document outlines the methodology, including preprocessing steps like subtracting the mean face, and reviews prior work applying PCA and other algorithms to face recognition.
IRJET- Result on the Application for Multiple Disease Prediction from Symptom...IRJET Journal
This document presents a system for predicting multiple diseases using symptoms and images with fuzzy logic. It discusses:
1. Creating a database by applying fuzzy rules to symptoms and labeled images provided by experts. This is the training phase.
2. Allowing users to enter symptoms or upload images for testing. The system analyzes the inputs using k-means clustering and fuzzy logic to predict the most likely diseases.
3. Experimental results showing the proposed system achieves higher accuracy (90%) and faster prediction times compared to existing methods. It can predict diseases from both symptoms and images to assist patients.
The document summarizes 5 papers from Zhejiang University of Finance and Economics that were included in the Ei Compendex database in 2005. It provides the title, authors, source, and brief summaries for each of the 5 papers.
A Comparison of Traditional Simulation and MSAL (6-3-2015)Bob Garrett
This document compares traditional simulation approaches to the Model-Simulation-Analysis-Looping (MSAL) approach. It provides background information on system modeling and simulation basics, including conceptual models, simulation programs, sensitivity analysis, Monte Carlo methods, and simulation optimization. It then discusses risk and uncertainty, modeling systems of systems, and the current state of modeling and simulation in systems engineering. Finally, it introduces the MSAL approach, which uses graphs, analytics, and repeated simulation loops to address the increased complexity and uncertainty in systems of systems compared to traditional approaches. The MSAL approach aims to provide benefits like improved handling of uncertainty and complexity.
A Hierarchical Feature Set optimization for effective code change based Defec...IOSR Journals
This document summarizes research on using support vector machines (SVMs) for software defect prediction. It analyzes 11 datasets from NASA projects containing code metrics and defect information for modules. The researchers preprocessed the data by removing duplicate/inconsistent instances, constant attributes, and balancing the datasets. They used SVMs with 5-fold cross validation to classify modules as defective or non-defective, achieving an average accuracy of 70% across the datasets. The researchers conclude SVMs can effectively predict defects but note earlier studies using the NASA data may have overstated capabilities due to insufficient data preprocessing.
For the agriculture sector, detecting and identifying plant diseases at an early stage is extremely important and
still very challenging. Machine learning is an application of AI that helps us achieve this purpose effectively. It
uses a group of algorithms to analyze and interpret data, learn from it, and using it, smart decisions can be
made. For accomplishing this project, a dataset that contains a set of healthy & diseased plant leaf images are
used then using image processing we extract the features of the image. Then we model this dataset with
different machine learning algorithms like Random Forest, Support Vector Machine, Naïve Bayes etc. The aim is
to hold out a comparative study to spot which of those algorithm can predict diseases with the at most
accuracy. We compare factors like precision, accuracy, error rates as well as prediction time of different
machine learning algorithms. After all these comparison, valuable conclusions can be made for this project.
2. and dynamic ways for pathway visualization and simulation. Others employ Ordinary Differ-
ential Equations (ODE) as their mathematical method of modeling. This approach is more
complex to use and larger ODE models (above 130 nodes) are more difficult to build and man-
age, and may also require dedicated computational facilities. These shortcomings further un-
derscore the need for the development of novel and more accessible tools for bench scientists.
Such tools, if available, would greatly accelerate the discovery process by: i) introducing bench
scientists to modeling of biological systems in an interactive graphical way; ii) allowing for hy-
pothesis testing at the early stage in the laboratory; iii) providing dynamic visualization of data
in the context of signaling pathways; iv) enabling visualization of perturbations within the
pathways by observing changes in pathway graphical display in an interactive and dynamic
way; v) facilitating ad-hoc preliminary modeling of alternative network circuitry and thus de-
termination whether further experimentation or more rigorous modeling is needed. We postu-
late that there is a broad and still unmet need to process, analyze/model and interpret
biological information at the early stage of data acquisition and analysis. Combined with a
user-friendly format that is accessible to an average health professional and scientist not
trained in mathematics/statistics and programming, such tools may lower the barrier to entry
for researchers who can be empowered to analyze their conceptual models and thus provide a
potential boost for discovery process in biomedicine.
In this report we describe our attempt to create such a software tool based on a standardized
approach using causal mapping (CMAP). In this approach, first introduced in 2006 [1], all ele-
mental influences assumed for each biochemical reaction, in essence directly corresponding to
ODE expressions, are graphically accounted for. Thus, the user may create a graphical repre-
sentation of the biological network at high level of detail, apply parameters and visually moni-
tor the network simulations in an interactive and dynamical manner. To test the performance
of Visinets graphical approach we have; a) built the “de novo” model of EGFR and Erk1/2 sig-
naling with manual parameter adjustment and; b) translated existing model of insulin signaling
in diabetes from published ODE model [2] into CMAP formalism.
Materials and Methods
Graphical representation of CMAP
The basic idea of the causal mapping is to graphically represent all the components of the sys-
tem of interest, along with all interactions between them, with an underlying aim to replace
mathematical descriptions with graphical equivalents. While it requires simplification and gen-
eralization of the mathematical framework, it provides the power of graphical user-computer
interaction particularly for users without rigorous training in mathematics of physics and
chemistry. Fig 1 illustrates the basic principles of the causal mapping and their graphical depic-
tion. In the context of biochemical reactions that take place in signaling pathways, these princi-
ples are translated into graphical representation of protein-protein interactions (for example
receptor-ligand interaction) and/or enzymatic reactions (for example protein phosphorylation)
shown in Fig 2 and Fig 3.
Analytical description of CMAP
A causal map is a graph in which the components (concepts) are the nodes (species) and causal
influences are the edges. The behavior of each species Cj(t) is described by the following basic
Visinets: A Dynamic Pathway Modeling Tool
PLOS ONE | DOI:10.1371/journal.pone.0123773 May 28, 2015 2 / 15
Competing Interests: JS, GEW, and PS are
employees of Visinets, Inc., and received salary from
them. This does not alter the authors' adherence to
all PLOS ONE policies on sharing data and materials.
3. CMAP equations for a network of N nodes:
CjðtÞ ¼ Cjðt À 1Þ þ LjðCjðt À 1Þ; fjÞ Ã fjðWij; Ciðt À 1ÞÞ ð1AÞ
fj f ðxjÞ ¼
1 À eÀajxj
1 þ eÀajxj
; xj ¼
XN
i¼1
CiWij þ
XN
i¼1
XN
k¼1
CiCkWikj þ . . . þ
XN
i¼1
. . .
XN
l¼1
Ci . . . ClWi...lj;
LjðCjðtÞ; fjÞ ¼
(
Cmax
j À CjðtÞ; if fj 0;
CjðtÞ; if fj 0;
ð1BÞ
The species (Cj(t)) can assume any value between 0 and Cj
max
( 1) and are variables; they could
represent species concentration and/or combination of concentration and activity. The weights
Fig 1. Elemental influences in Visinets. To achieve shown behavior in Visinets, all initial values for species
A and P were set as shown and the weights for all influences (arrows) were set at 0.5, except for positive self-
influence (E) where the influence of A on P was set at 0.01.
doi:10.1371/journal.pone.0123773.g001
Visinets: A Dynamic Pathway Modeling Tool
PLOS ONE | DOI:10.1371/journal.pone.0123773 May 28, 2015 3 / 15
4. (Wij, strengths of influences reflecting the ODE rate constants, e.g. k1, k2, and kcat) are constant dur-
ing simulations. The absolute values of the weights are also normalized and limited to the same in-
terval (0,1). Each influence may be assigned positive or negative value (activation or inhibition) and
carry positive or negative numerical value depicted in green or red, respectively (Fig 2 and Fig 3).
Multiple inputs are added and/or subtracted, depending on their values. Most species have their ini-
tial (Cj(t)) value assigned zero, e.g. they are not active unless activated by another species (cause).
The right side of Eq (1A) contains two factors. Ʌj restricts the species to the 0–1 scaled interval. The
causal function fj includes all influences from the system (including self-influence, red line attached
to one species only), as depicted in Fig 2 and Fig 3, on a given species Cj(t). Parameter α in Eq (1B)
(Fig 4) determines the sensitivity of a response of a species to a given input. In other words, α may
be used, within the suggested range between 0.5 to 5 (default is set at 1.2), to increase the species re-
sponsiveness to change in input.
Software
The front-end (GUI) and back-end of the Visinets software was developed using the Google
Development Kit and Dart. Visinets uses Google's Big Table database to store all of the data
that the software will create including: experimental data, models, graph visualizations, user in-
formation and analytics, references and other data. Visinets also employs Google's App Engine
to host the application and APIs through multiple RESTful APIs that allow the front-end client
to access, modify, and search data on the database. In addition, we use Amazon's Elastic Com-
pute Cloud (ec2) (written in C/C++) to power the simulations. The web-based implementation
Fig 2. Comparative representation of binding reaction, represented here by EGFR-EGF interaction,
using ODE and CMAP formalism. Translation scheme of a chemical kinetics formalism based on ordinary
differential equations (A) into a CMAP representation (B), with a full one-to-one translation protocol of all ODE
terms into influences (C). The reaction rate constants (k) from ODE are represented in CMAP by weights.
CMAP representation allows for second order influences (e.g., k1*[R]*[EGF]) to reflect multiple causal origin
of a concept’s change.
doi:10.1371/journal.pone.0123773.g002
Visinets: A Dynamic Pathway Modeling Tool
PLOS ONE | DOI:10.1371/journal.pone.0123773 May 28, 2015 4 / 15
5. ensures platform-free execution throughout all the available devices, from PC to tablets and
smart phones.
Visinets graphical user interface
Visinets graphical interface employs HTML5 technology to ensure cross-platform compatibili-
ty and takes advantage of its features: the Canvas to draw hardware accelerated pathway maps
and graphs; application cache and local storage to use the software when not connected to the
internet; and file-reader and drag and drop API's to efficiently use local files. Users may use
Visinets in two modes: a) Build/Modify mode, where the user can construct the pathway using
graphical means and manually apply initial values for parameters, and perform simulations;
and b) Parameter Search mode, where user can perform global parameter search using user-
selected rules.
In Build/Modify mode, users can take full advantage of the graphical environment and
draw all components of the biological network in a causally linked manner, using both positive
and negative influences (arrows/connectors). Three or four species that form a specific reaction
module may be used to build signaling pathway in a more automated way. Larger network
blocks, representing multiple biochemical reactions, can be selected, moved around and/or
copied and pasted into new pathway files. Left side pull-out menus provide the list of all species
and influences and pop-up menus for each species and influence allowing to visually inspect
and edit parameters, initial conditions and graphical appearance. Several graphical features
have been developed for easier workflow and more attractive visualization of signal
Fig 3. Graphical principles of conversion of an enzymatic reaction from a chemical kinetics formalism
based on ordinary differential equations (A) into a CMAP representation (B), with full one-to-one
translation scheme of all ODE terms into influences (C).
doi:10.1371/journal.pone.0123773.g003
Visinets: A Dynamic Pathway Modeling Tool
PLOS ONE | DOI:10.1371/journal.pone.0123773 May 28, 2015 5 / 15
6. transduction throughout the network; a) clone function to allow multiple copies of the same
species to be associated with different parts of the network and thus avoiding the dense net-
work of intersecting connections throughout the working area; b) dynamic changing of the
thickness of the influence connector (arrow) in proportion to the corresponding signal intensi-
ty; and c) dynamic changing of the transparency of species that is inversely proportional to the
C value (activity/concentration). Each network can be shown in Basic View with essential con-
nections only showing the causality and direction of signal transduction, which is recom-
mended for simulations and visualization and in Full View with all influences shown,
recommended for model building, editing and/or parameter adjustments. (Supporting Infor-
mation: S1 Visinets Software: Visinets software is freely available to general public as an online
resource at http://www.visinets.com/index.htm with full user documentation (User Guide and
Help) and a list of executable signaling pathways with embedded parameter datasets (fully
user-adjustable), including those described in this report (http://www.visinets.com/pathway/
list))
Parameter Search
In Parameter Search mode the user can apply a set of rules (criteria) to search for parameters
(initial conditions of species, Cini, and weights) automatically. The rules currently available in
Visinets include Increase, Decrease, Transient up, Transient down, and Oscillations and may
be set for any species. Once initiated, the search will stop at the first randomly generated set of
parameters that satisfy the chosen rules and results can be viewed using the Plot function. If
Fig 4. Causal function. It describes causal interactions through concepts and weights. The steepness
of the function is determined by parameter α: the larger the value of α, the more responsive f(x) is to the input.
doi:10.1371/journal.pone.0123773.g004
Visinets: A Dynamic Pathway Modeling Tool
PLOS ONE | DOI:10.1371/journal.pone.0123773 May 28, 2015 6 / 15
7. the result is not satisfactory the user can repeat the search for a new set of parameters or con-
tinue with manual adjustments.
Results
To test the performance of Visinets software we have created two models of signaling path-
ways. First, we have re-constructed the model of EGFR signaling complex with MAPK kinase
pathway and manually selected parameters to satisfy specific criteria. Second, we have translat-
ed a published ODE model of Insulin signaling using one-to-one mapping of all reactions and
compared the results with the original ODE model.
EGFR-MAPK signaling model
We have constructed a 70-node EGFR and MAPK signaling pathway with associated phospho-
protein phosphatases MKP and PTB1B (including 29 biochemical reactions, 229 influences
and 22 clones) (Fig 5). The network of EGFR and MAPK signaling was constructed to model
the behavior of combined MAPK, MKP and PTP1B feedback inhibition on EGFR signaling
[3–6]. The core pathway consists of 2 initial branches of EGFR signaling, Grb2-PTP1B (phos-
photyrosine phosphatase feedback) and Shc-Grb2-Sos1 (MAPK pathway). Protein adaptors
Sos, Grb2 and Shc are shown within their extensive network of interactions as described in [7].
Protein phosphatase PTP1B binds to EGFR through Grb2 adaptor protein to terminate
EGFR signaling and protein phosphatase MKP2 terminates the Erk1/2 signaling. The pathway
illustrates Erk1/2 feedback negative regulation of several species, including Mek1/2, Raf1 (c-
Raf), Receptor-Shc-Grb2-Sos1 (RSGS) complex and tyrosine phosphorylated receptor (RP).
The delayed Ser/Thr phosphorylation of EGFR by Erk1/2 remains more controversial and may
be indirect. Feedback phosphorylation-inhibited species do accumulate and are named
iRShGS, iR, iRaf and iMek and may be subject for dephosphorylation and recycling, or for in-
ternalization and degradation (not included in this model). Erk1/2 activity itself is terminated
by protein phosphatase MKP (DUSP4). The criteria used to re-construct the core network be-
havior were the transient phosphorylation (activation) for RP, RSGS, Rafpp and Erkpp, gener-
ated by EGFR and several negative feedbacks of Erk1, PTP1B and MKP activities. This
behavior, including relative time delay, recapitulates some of the typical regulation that is com-
mon for EGFR and MAPK pathways [3–6] (Fig 6).
The manual parameter selection strategy applied for this model involved three guiding prin-
ciples, each helped by the visual analysis of signal propagation throughout the network during
execution of simulation; a) slow reaction (low weight value) following fast reaction will cause
the relative accumulation of the reaction intermediate; a) having only same type of inputs (“ac-
tivation” or “inhibition”) can cause only corresponding monotonic change; b) to obtain “tran-
sient” (non-monotonic) response there should be at least two inputs but of different type; c)
several rounds of adjustments were applied to find each weight value in the most sensitive
range defined as a smallest weight adjustment made to cause the largest change in activity, and
d) final execution of simulation with visual determination of node transparency (as a graphical
representation of activity/concentration) to identify potential remaining signaling roadblocks
or diversions. These adjustments did not need to be exhaustive, in the above case the criteria
were satisfied within 2 hours of manual weight parameter manipulations. The α parameter was
left at default value of 1.2 for all but RafPP and ErkPP, in which case it was set at 4 to enhance
its responsiveness to input and induce a more pronounced”switch-like” behavior. The sum of
these adjustments produced RafPP responding to EGF levels in a concentration dependent
manner. However, the less responsive way of ErkPP may be due to incomplete negative regula-
tion of activated Erk1/2 represented in our scheme.
Visinets: A Dynamic Pathway Modeling Tool
PLOS ONE | DOI:10.1371/journal.pone.0123773 May 28, 2015 7 / 15
8. Fig 5. EGFR signaling pathway shown in working space of Visinets in Build/Modify mode and in
default Basic View. Note the use of clones for selected species, for example for RP (phosphorylated EGFR),
ErkPP (double phosphorylated Erk1/2), etc. that allow the placement of the same species in different
locations, thus eliminating a dense network of crossing connections and producing a “cleaner” pathway
representation. The linear “core” EGFR signaling pathway is shown in light green, adaptor proteins (grb2,
Shc, SOS) and their complexes (for example GS which represents Grb2 and SOS complex) are shown in
dark green, and protein phosphatases (PTP1B, activated form PTP1Ba and MKP), their inactive complexes
after phosphorylation, and inactive phosphorylated Receptor, RShGS, Raf and Mek (iR, iRShGS, iRaf and
iMek), are shown in red. This executable model is available with embedded parameter dataset at http://www.
visinets.com/pathway/list in Featured Pathways (Supporting Information: S1 Visinets Pathway: “EGFR
signaling complex with MAPK and PTP1B feedback inhibition”).
doi:10.1371/journal.pone.0123773.g005
Fig 6. Transient activation (tyrosine or serine/threonine phosphorylation) of EGFR and MAPK
pathway components shown in Visinets using Plot function. Each single iteration equates to the smallest
incremental signal progression through the network (virtual time). R2—receptor dimer; Rp—phosphorylated
receptor; RShP—Receptor-Shc-P; RShGS—Receptor-Shc-Grb-Sos complex; RafPP—phosphorylated raf1;
MekPP—phosphorylated Mek1/2; ErkPP—phosphorylated Erk1/2.
doi:10.1371/journal.pone.0123773.g006
Visinets: A Dynamic Pathway Modeling Tool
PLOS ONE | DOI:10.1371/journal.pone.0123773 May 28, 2015 8 / 15
9. Insulin signaling in diabetes
To further test the performance of Visinets, and to directly compare our approach to ODE meth-
od, we used an existing ODE model of insulin signaling in normal and type 2 diabetes (T2D) cells
[2] and translated it, ODE expression-to-CMAP influence (one-to-one), using the general scheme
described above in Fig 2 and Fig 3. The Table 1 lists all the corresponding reactions we used to re-
create the model in Visinets. Since the original model has been intentionally reduced to 27 species
and deviates somewhat from strict mass-action formalism, the resulting CMAP representation is
simplified accordingly. The re-created insulin signaling pathway is shown in Fig 7.
Parameter search for insulin signaling model
To obtain parameter sets for the simulation of Insulin pathway in normal and pathological
states and to compare the ODE and CMAP simulations directly, we exported CMAP code
from Visinets and run parameter search and simulations in Matlab (MathWorks, Inc.). The
transient phosphorylation of Insulin Receptor and IRS1 were used as search rules and the
search was conducted in a step-wise manner:
1. Export the model from Visinets (File/Export Model) to create a programing code for
further simulations.
2. Generate the 1 million random sets of weights (corresponding to rate constants in the ODE
model) so that max(W)/min(W) = 1000 and α-s (0.5–3) and the values were evenly distrib-
uted in these ranges.
3. Run simulation for each weight set until steady state was achieved with the initial values for
the species shown in the Table 1 for both normal and T2D cells. The differences between
the two types of cells were (in correspondence with Figure 1 in [2]):
IR(T2D) = 0.55Ã
IR;
GLUT4(T2D) = 0.5Ã
GLUT4;
Diabetes (T2D) = 0.15Ã
Diabetes;
4. After achieving the steady states values for all species, we used those as initial conditions to
run the simulation in the presence of insulin (insulin = 1) and tested for two rules: transient
response for measuredIRp = IRp+IRip and measuredIRS1p = IRS1p + IRS1p307 for both
normal and T2D conditions. Representative simulations qualitatively confirming the find-
ing from the original ODE model are shown in Fig 8.
5. Using obtained parameters sets that satisfy the above rules, we found those that belong to
both normal and T2D ensembles and saved them for further analysis.
6. We ran these sets to satisfy observations presented in Fig 5B from [2]: the responses curves
from normal cells lie higher than the ones for the T2D cells. The satisfaction of these two
rules was the final point of our selection process.
It is also important to note that to achieve pathway behavior as described in the original
paper [2], the Visinets user may also follow the guidelines described for the EGFR-MAPK path-
way described above, and manually find the parameter set that satisfies the original criteria.
Computational efficiency
We have compared computational efficiency of simulations with CMAP and ODE approaches
by running simulations of 3 different biological pathways: Insulin signaling in the Normal state
as described above, EGFR signaling pathway as described by Kholodenko co-authors [4],
Visinets: A Dynamic Pathway Modeling Tool
PLOS ONE | DOI:10.1371/journal.pone.0123773 May 28, 2015 9 / 15
11. and EGFR signaling as described by Borisov co-authors [8]. The comparison was performed
using build-in feature of Matlab. The computational efficiency, as defined by time per iteration
point and calculated as a ratio of ODE simulation time to CMAP simulation time, varied from
4.29 for Insulin signaling model, to 9.13 for EGFR model [8], and to 64.25 for EGFR model [4].
This comparison shows a significant advantage of CMAP models when compared head to
head in computational efficiency.
Discussion
The underlying mathematical approach in Visinets (Causal Mapping (CMAP)) is a modeling
method that allows for the integration of diverse data into a single model of networks organized
in causally linked relationships. Initially applied to model of cortical contraction [1, 9], CMAP
is a semi-quantitative approach based on a detailed description of all network interactions and
is particularly useful when detailed knowledge of those interactions is absent and specific pa-
rameter values need to be estimated. CMAP approach lies between coarse-grained (e.g. Bool-
ean and Petri nets) methods, and highly detailed mechanistic (e.g. ODE based) methods
(Table 2). As with ODEs, CMAP allows for continuous modeling with standardized kinetic
equations for each species and thus exhibiting significant advantage over the simpler discrete
Table 1. (Continued)
# Component (derivative) ODE Expressions CMAP influences Initial values
17 mTORC1a mTORC1 * k5a1 * PKB308p473p + mTORC1 * PKB308p473p 0
+ mTORC1 * k5a2 * PKB308p + mTORC1* PKB308p
- mTORC1a * k5b - mTORC1a
18 mTORC2 k5d * mTORC2a + mTORC2a 0.9
- mTORC2 * k5c * IRip - mTORC2 * IRip
19 mTORC2a mTORC2 * k5c * IRip + mTORC2* IRip 0
- k5d * mTORC2a - mTORC2a
20 AS160 AS160p * k6b + AS160p 0.7
- AS160* k6f1 * PKB308p473p - AS160* PKB308p473p
- AS160*k6f2 * PKB473pn6
/(km6n6 + PKB473pn6
) - AS160* PKB473p
21 AS160p AS160* k6f1 * PKB308p473p + AS160* PKB308p473p 0
+ AS160* k6f2 * PKB473pn6
/(km6n6 + PKB473pn6
) + AS160* PKB473p
- AS160p * k6b - AS160p
22 GLUT4m GLUT4 * k7f * AS160p + GLUT4* AS160p 0
- GLUT4m * k7b - GLUT4m
23 GLUT4 GLUT4m * k7b + GLUT4m 0.6
- GLUT4 * k7f * AS160p - GLUT4* AS160p
24 S6K S6Kp * k9b1 + S6Kp 0.9
- S6K * k9f1 * mTORC1an9
/(km9n9
+ mTORC1an9
) - S6K * mTORC1a
25 S6Kp S6K * k9f1 * mTORC1an9
/(km9n9
+ mTORC1an9
) + S6K * mTORC1a 0
- S6Kp * k9b1 - S6Kp
26 S6 S6p * k9b2 + S6p 0.9
- S6 * k9f2 * S6Kp - S6 * S6Kp
27 S6p S6 * k9f2 * S6Kp + S6 * S6Kp 0
- S6p * k9b2 - S6p
28 I (insulin) 0
29 diabetes 1
doi:10.1371/journal.pone.0123773.t001
Visinets: A Dynamic Pathway Modeling Tool
PLOS ONE | DOI:10.1371/journal.pone.0123773 May 28, 2015 11 / 15
12. Fig 7. Simplified Insulin signaling pathway, reconstructed in Visinets as described in [2]. This
executable model is available with embedded parameter datasets at http://www.visinets.com/pathway/list in
Featured Pathways (Supporting Information: S2 Visinets Pathway: “Insulin signaling in Diabetes”).
doi:10.1371/journal.pone.0123773.g007
Fig 8. Transient activation (tyrosine phosphorylation) of IR and IRS1. The phosphorylated species
shown are the sum of multiple forms of both IR and IRS1.
doi:10.1371/journal.pone.0123773.g008
Visinets: A Dynamic Pathway Modeling Tool
PLOS ONE | DOI:10.1371/journal.pone.0123773 May 28, 2015 12 / 15
13. Boolean and Rule Based methods which can’t be parameterized and thus describe the model
behavior with significantly lower level of detail [10].
CMAP permits the investigation of system’s dynamics, with all its elements connected in a
biologically and causally meaningful way (as with differential equation approaches) and is tol-
erant to knowledge gaps, discrepancies and uncertainties. Since this standardized method for
pathway modeling can easily be executed within the user-friendly graphical interface, it allows
for an intuitive and non-technical network analysis for bench biologists, geneticists and health
professionals. Although other standardized mathematical approaches have been proposed [10,
11], they were not developed into fully user-friendly and graphically oriented software pack-
ages. There are few software packages available that are user-friendly (e.g., Cell Collective [12]
or RuleBender [13]) but they are using other modeling methods such as rule-based modeling
and/or Boolean algebra and thus having their own limitations. Other known tools such as
Cytoscape or Ingenuity Pathway Analysis are very useful for data mining, pathway graphical il-
lustration, however they lack the power of simulation and dynamic visualization.
To evaluate the “ease of use” and simulation performance, and potentially broader user ap-
peal in biomedical research, we have successfully re-created the behavior of EGFR and MAPK
pathway by employing manual selection of weight parameter for each reaction. Such method
may also be used for refining pathway behavior after initial automated parameter search.
Though in some cases the manual method may seem tedious, the process of weight (i.e., reac-
tion rate constants) adjustments itself may provide the user with an intuitive way for better un-
derstanding of pathway dynamics, relationships between different sections of the networks,
and mechanistic relationships such as positive and negative feedback loops. In fact, this more
intuitive manual way of model refinement and associated benefit of learning pathway dynamics
may better help the user to discern between similar network topologies and settings and find
the most optimal network circuitry for further experimentation and analysis. As such Visinet’s
dynamic computational models can be considered a means of conceptual model verification,
by which models generated by researchers from the understanding of their research field can
be verified computationally and the outcomes simulated, and thus the behavioral consequences
of the researcher’s proposed topology/hypothesis can be evaluated in an easy to follow graphi-
cal way [14]. This way of using Visinets could also serve as potentially great training/education-
al tool. Visinets free online access offers users several additional signaling pathways to further
illustrate the potential of the software and its research application for the broader community
of biomedical bench scientists.
The direct comparison of insulin ODE and CMAP models provides strong evidence that
both approaches give very similar outcomes. Furthermore, direct comparison of computational
efficiency for 3 independent models show the clear advantage for CMAP approach for use in
simulations of biological pathways on desktop computers. The insulin signaling pathway, stud-
ied in more detail above, is also an example of using an existing ODE model and importing it
into Visinets. If such model is available, the translation into Visinets may be relatively straight-
forward and allows the user to generate simulations and dynamic pathway illustrations within
Table 2. Causal mapping represents an approach with intermediate level of detail relative to that re-
quired for differential equations and Boolean network approaches.
Comparison of CMAP with other approaches
Highly Coarse
Grained
Detailed Coarse
Grained
Highly Detailed
Boolean; Petri nets Causal mapping
(CMAP)
Differential Equations: Chemical kinetics; Mechanics;
Stochastic physics
doi:10.1371/journal.pone.0123773.t002
Visinets: A Dynamic Pathway Modeling Tool
PLOS ONE | DOI:10.1371/journal.pone.0123773 May 28, 2015 13 / 15
14. the extensive graphical capabilities of Visinets. Furthermore, user may import and then modify,
add or delete species (nodes) or influences (edges) or even combine smaller models into larger
one within the Visinets platform. The software module of ODE model import function into
Visinets is being currently developed. In a reverse process, if necessary, the Visinets model/net-
work can also be exported into more capable software packages where more extensive parame-
ter search and parameter space study using qualitative rules could be performed. Importantly,
Visinets will continue to extend the range of statistical tools and graphical features in Visinets
modeling software, thus potentially alleviating the need for further analysis in other software
packages.
Due to its graphical capabilities, the usefulness of Visinets may be extended beyond model-
ing of signaling pathways, and into dynamic visualization of other biological processes. Thus,
the modeling capability of Visinets, in combination with attractive visualization, may offer a
unique way for the integrated graphical analysis of different biological scenarios and also use it
for presentation purposes.
In this version Visinets may be a particularly useful tool for semi-quantitative analysis of
pathway models that are larger than 50–100 nodes, a large size considered for computationally
more demanding ODE models. With the zoom function currently in place, Visinets graphical
working space can practically accommodate pathway models larger than 300 nodes.
Supporting Information
S1 CMAP Code. The code for Visinets CMAP modeling engine has been deposited for
free use at https://github.com/paulspychala/CMAPdart under open source MIT license.
S1 Visinets Pathway. EGFR signaling complex with MAPK and PTP1B feedback inhibi-
tion. This pathway model has parameter dataset embedded in the pathway and is listed in Fea-
tured Pathways at http://www.visinets.com/pathway/list.
S2 Visinets Pathway. Insulin signaling in Diabetes. This pathway model has parameter
datasets embedded in the pathway and is listed in Featured Pathways at http://www.visinets.
com/pathway/list.
S1 Visinets Software. Visinets software is freely available to general public as an online re-
source at http://www.visinets.com/index.htm with full user documentation (User Guide and
Help) and a list of executable signaling pathways with embedded parameter datasets (fully
user-adjustable), including those described in this report (http://www.visinets.com/pathway/
list).
Author Contributions
Conceived and designed the experiments: JS PS GEW. Performed the experiments: JS PS
GEW. Analyzed the data: JS GEW. Contributed reagents/materials/analysis tools: SG. Wrote
the paper: SG. Designed the Visinets software and wrote the code: PS.
References
1. Weinreb GE, Elston TC, Jacobson K. Causal mapping as a tool to mechanistically interpret phenomena
in cell motility: Application to cortical oscillations in spreading cells. Cell Motil Cytoskeleton 2006; 63:
523–532. PMID: 16800006
2. Brannmark C, Nyman E, Fagerholm S, Bergenholm L, Ekstrand EM, Cedersund G, et al. Insulin signal-
ing in type 2 diabetes: experimental and modeling analyses reveal mechanisms of insulin resistance in
human adipocytes. J Biol Chem. 2013; 288: 9867–9880. doi: 10.1074/jbc.M112.432062 PMID:
23400783
3. Ferrari E, Tinti M, Costa S, Corallino S, Nardozza AP, Chatraryamontri A, et al. Identification of New
Substrates of the Protein-tyrosine Phosphatase PTP1B by Bayesian Integration of Proteome Evidence.
J Biol Chem. 2011; 286: 4173–4185. doi: 10.1074/jbc.M110.157420 PMID: 21123182
Visinets: A Dynamic Pathway Modeling Tool
PLOS ONE | DOI:10.1371/journal.pone.0123773 May 28, 2015 14 / 15
15. 4. Kholodenko BN, Demin OV, Moehren G, Hoek JB. Quantification of short term signaling by the epider-
mal growth factor receptor. J Biol Chem. 1999; 274: 30169–30181. PMID: 10514507
5. Tautz L, Critton DA, Grotegut S. Protein Tyrosine Phosphatases: Structure, Function, and Implication
in Human Disease. Methods Mol Biol. 2013; 1053: 179–221. doi: 10.1007/978-1-62703-562-0_13
PMID: 23860656
6. Zheng Y, Zhang CJ, Croucher DR, Soliman MA, St-Denis N, Pasculescu A, et al. Temporal regulation
of EGF signaling networks by the scaffold protein Shc1. Nature 2013; 499: 166–171. doi: 10.1038/
nature12308 PMID: 23846654
7. Saraiya P, North C, Duca K. An insight-based methodology for evaluating bioinformatics visualizations.
IEEE Trans Vis Comput Graph. 2005; 11: 443–56. PMID: 16138554
8. Borisov N, Aksamitiene E, Kiyatkin A, Legewie S, Berkhout J, Maiwald T, et al. Systems-level interac-
tions between insulin-EGF networks amplify mitogenic signaling. Mol Syst Biol. 2009; 5: 256. doi: 10.
1038/msb.2009.19 PMID: 19357636
9. Weinreb GE, Kapustina MT, Jacobson K, Elston TC. In Silico Generation of Alternative Hypotheses
Using Causal Mapping (CMAP). PLoS ONE 2009; 4: e5378. doi: 10.1371/journal.pone.0005378 PMID:
19401774
10. Di Cara A, Garg A, De Micheli G, Xenarios I, Mendoza L. Dynamic simulation of regulatory networks
using SQUAD. BMC Bioinformatics 2007; 8: 462. PMID: 18039375
11. Nijhout HF, Callier V. A new mathematical approach for qualitative modeling of the insulin-TOR-MAPK
network. Front. Physiol. 2013; 4: 245. doi: 10.3389/fphys.2013.00245 PMID: 24062690
12. Helikar T, Kowal B, McClenathan S, Bruckner M, Rowley T, et al. The Cell Collective: Toward an open
and collaborative approach to systems biology. BMC Syst Biol. 2012; 6: 96. doi: 10.1186/1752-0509-6-
96 PMID: 22871178
13. Smith AM, Xu W, Sun Y, Faeder JR, Marai GE. RuleBender: integrated modeling, simulation and visu-
alization for rule-based intracellular biochemistry. BMC Bioinformatics 2012; 13: 8–S3. doi: 10.1186/
1471-2105-13-8 PMID: 22239737
14. MacLeod M, Nersessian NJ. Modeling systems-level dynamics: Understanding without mechanistic ex-
planation in integrative systems biology. Stud Hist Philos Biol Biomed Sci. 2015; 49: 1–11. doi: 10.
1016/j.shpsc.2014.10.004 PMID: 25462871
Visinets: A Dynamic Pathway Modeling Tool
PLOS ONE | DOI:10.1371/journal.pone.0123773 May 28, 2015 15 / 15