Data Mining Using a Consensus Algorithm
•
1 like•502 views
1) Researchers developed a consensus fingerprint data mining method using 400 medicinally relevant functional groups to classify molecules and mine databases. 2) The method was validated on a dataset of over 300,000 compounds classified into 538 biological target classes, showing an average of 37.7-87.9% of active compounds recovered within the top hits. 3) Using a consensus approach that overlapped the functional group fingerprint predictions with Daylight fingerprint predictions resulted in a 50% reduction in false positives compared to the individual methods.
Report
Share
Report
Share
![Development Goals ,[object Object],[object Object],[object Object],[object Object],Goals:](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
Recommended
CHEM0260_McPhillips_S
The document summarizes an experiment using Fourier Transform Infrared Spectroscopy with Attenuated Total Reflectance (FTIR-ATR) to quantify the amount of caffeine in various over-the-counter supplements. Standard solutions with known caffeine concentrations were prepared and used to construct a calibration curve. Test samples of caffeine supplements and weight loss pills were then analyzed to determine their caffeine content and results were found to have relative errors ranging from 38-50% compared to actual values. The purpose was to quantify caffeine in supplements and the method showed potential for further studies of different brands.
1 statistical analysis
This document summarizes the analysis of different extraction and drying protocols for pumpkin leaf metabolites. It compares various metrics like mean, standard deviation, and percent relative standard deviation (%RSD) to identify the optimal sample processing method. Method #2, using acetonitrile, isopropanol and water in a 3:3:2 ratio, showed the lowest average %RSD and most metabolites below a 40% RSD cutoff, making it the optimal method. Statistical tests found minimal effect of drying on metabolite levels. A power analysis determined sample sizes needed to detect certain minimum effect sizes.
Mapping to the Metabolomic Manifold
Description of network mapping and data visualization approaches for metabolomic and genomic data integration.
LE Metrics (EuroQSAR2016)
This document discusses issues with commonly used ligand efficiency metrics. It argues that ligand efficiency metrics make unrealistic assumptions by normalizing potency based on trends not actually observed in data. Specifically, ligand efficiency assumes a linear relationship between potency and risk factors like lipophilicity, but data does not always support this assumption. It also notes that ligand efficiency incorporates arbitrary concentration units that can affect calculated values. The document suggests plotting affinity against risk factors to test the assumptions behind ligand efficiency metrics.
Multivarite and network tools for biological data analysis
See video:
http://imdevsoftware.wordpress.com/2014/06/27/multivariate-data-analysis-and-visualization-through-network-mapping/
RSC Hatfield 2018 Kinase meeting : potency patents MMPA approaches
Demonstration of how applying MMPA analysis and network analytics can help identify SAR in kinase chemical space.
Prote-OMIC Data Analysis and Visualization
Introductory lecture to multivariate analysis of proteomic data.
Material from the UC Davis 2014 Proteomics Workshop.
See more at: http://sourceforge.net/projects/teachingdemos/files/2014%20UC%20Davis%20Proteomics%20Workshop/
Griffen MedChemica Virtual Tox Panel
Virtual Toxicity panels focussed on interpretable machine learning models that can guide medicinal chemists to identify critical substructures that are assocaited with toxicities.
Recommended
CHEM0260_McPhillips_S
The document summarizes an experiment using Fourier Transform Infrared Spectroscopy with Attenuated Total Reflectance (FTIR-ATR) to quantify the amount of caffeine in various over-the-counter supplements. Standard solutions with known caffeine concentrations were prepared and used to construct a calibration curve. Test samples of caffeine supplements and weight loss pills were then analyzed to determine their caffeine content and results were found to have relative errors ranging from 38-50% compared to actual values. The purpose was to quantify caffeine in supplements and the method showed potential for further studies of different brands.
1 statistical analysis
This document summarizes the analysis of different extraction and drying protocols for pumpkin leaf metabolites. It compares various metrics like mean, standard deviation, and percent relative standard deviation (%RSD) to identify the optimal sample processing method. Method #2, using acetonitrile, isopropanol and water in a 3:3:2 ratio, showed the lowest average %RSD and most metabolites below a 40% RSD cutoff, making it the optimal method. Statistical tests found minimal effect of drying on metabolite levels. A power analysis determined sample sizes needed to detect certain minimum effect sizes.
Mapping to the Metabolomic Manifold
Description of network mapping and data visualization approaches for metabolomic and genomic data integration.
LE Metrics (EuroQSAR2016)
This document discusses issues with commonly used ligand efficiency metrics. It argues that ligand efficiency metrics make unrealistic assumptions by normalizing potency based on trends not actually observed in data. Specifically, ligand efficiency assumes a linear relationship between potency and risk factors like lipophilicity, but data does not always support this assumption. It also notes that ligand efficiency incorporates arbitrary concentration units that can affect calculated values. The document suggests plotting affinity against risk factors to test the assumptions behind ligand efficiency metrics.
Multivarite and network tools for biological data analysis
See video:
http://imdevsoftware.wordpress.com/2014/06/27/multivariate-data-analysis-and-visualization-through-network-mapping/
RSC Hatfield 2018 Kinase meeting : potency patents MMPA approaches
Demonstration of how applying MMPA analysis and network analytics can help identify SAR in kinase chemical space.
Prote-OMIC Data Analysis and Visualization
Introductory lecture to multivariate analysis of proteomic data.
Material from the UC Davis 2014 Proteomics Workshop.
See more at: http://sourceforge.net/projects/teachingdemos/files/2014%20UC%20Davis%20Proteomics%20Workshop/
Griffen MedChemica Virtual Tox Panel
Virtual Toxicity panels focussed on interpretable machine learning models that can guide medicinal chemists to identify critical substructures that are assocaited with toxicities.
Day2_Plenary_Peter R Steward
Day2_Plenary_Peter R Steward CCAFS | CGIAR Research Program on Climate Change, Agriculture and Food Security
Presentation at the 5th Global Science Conference on Climate-Smart Agriculture.
Title: Synergy or Antagonism: Interactions among CSA practices in Africa
Speaker: Peter R Steward 3 principal components analysis
This document discusses using principal component analysis (PCA) to analyze metabolomic sample data from pumpkin experiments. PCA was performed on the raw data and scaled data to identify major sources of variance. For the raw data, the first two principal components captured most of the variance and separated samples by extraction method and treatment. Several samples were identified as potential outliers. When PCA was done on autoscaled data, the loadings showed differences due to both extraction and treatment. The scaled analysis also identified some outlier samples.
Data analysis workflows part 2 2015
previous: https://github.com/dgrapov/TeachingDemos/blob/master/Demos/Data%20Analysis%20Workflow/report/report.md
Connecting Metabolomic Data with Context
The document summarizes Dmitry Grapov's presentation on connecting metabolomic data with context. It discusses using network mapping and multivariate tools to analyze metabolomic data by generating connections between metabolites based on biochemical, chemical, and empirical relationships. These connections can help identify relationships between experimental observations and link the known with unknown. The presentation also provides examples of projects applying these techniques to analyze data from various disease studies involving changes in lipids, proteins, and small molecule metabolites.
Ligand efficiency: nice concept shame about the metrics
Ligand efficiency metrics are meant to normalize ligand activity with respect to molecular properties that increase risk, however they make assumptions about relationships that are not always valid. Residuals, which quantify the extent to which activity beats a trend line fit to the data, may be a better approach as they do not depend on standard concentration units or require defining a trend line a priori. Normalizing data requires accurately modeling the observed trends in a data set, and ligand efficiency metrics can distort perception if the trends are not properly considered.
Metabolomic Data Analysis Workshop and Tutorials (2014)
This document provides an introduction and overview of tutorials for metabolomic data analysis. It discusses downloading required files and software. The goals of the analysis include using statistical and multivariate analyses to identify differences between sample groups and impacted biochemical domains. It also discusses various data analysis techniques including data quality assessment, univariate and multivariate statistical analyses, clustering, principal component analysis, partial least squares modeling, functional enrichment analysis, and network mapping.
Accelerating lead optimisation with active learning by exploiting MMPA based ...
Presented at the 15th GCC - German Conference on Cheminformatics November 2019
We combine regression forest machine learning with our MMPA based generative methods to deliver an active learning system to accelerate lead optimisation. In the process we identify permutative MMPA as a method to leverage SAR information from small data sets.
Published by MedChemica Ltd
Data analysis and Visualisation Techniques for Compound Combination Modelling
A talk given at the EBI for the Cambridge Cheminformatics Network Meeting on the 25/11/2015, introducing compound combination modelling analysis and visualisation techniques.
Metabolomics and Beyond Challenges and Strategies for Next-gen Omic Analyses
Dr. Dmitry Grapov gave a webinar on challenges and strategies for next-generation omics analyses. He discussed how large, longitudinal studies integrating multiple omics domains are needed to identify small biological effects. Data normalization strategies must be considered during experimental design to remove analytical batch effects. Quality control-based normalization using analytical replicates can estimate and remove analytical variance from large datasets. Integrating multiple measurement platforms is often required to identify systems of biological changes. Network-based analysis of omics data can help explain more phenotypic variance than single omics approaches alone. Dr. Grapov demonstrated software tools he developed for network analysis, visualization, and integration of multi-omics datasets.
2 cluster analysis
The document discusses using hierarchical cluster analysis (HCA) to evaluate metabolomic sample processing methods. It describes two goals: 1) Use HCA to cluster samples based on raw data similarities and correlations to determine the impact of extraction and treatment methods on data variance. Extraction had the greatest effect, with ACN:/IPA/water and MeOH/CH3Cl/water samples most similar. 2) Use HCA to cluster metabolites based on z-scaled data and correlations to identify groups of related metabolites and evaluate the robustness of different correlation measures. Clusters extracted from the correlation-based dendrogram contained metabolites that shared biological functions.
IRJET- Classification of Chemical Medicine or Drug using K Nearest Neighb...
This document proposes using a combination of K-nearest neighbors (KNN) and genetic algorithms to classify chemical medicine or drug data with improved accuracy. KNN is described as a simple and effective classification algorithm that stores training data instances. Genetic algorithms are presented as evolutionary algorithms useful for optimization problems. The proposed system applies genetic search to rank attribute importance, selects high-ranked attributes, and then applies both KNN and genetic algorithms to classify the drug data, aiming to improve classification accuracy over using either technique alone. The combination of KNN and genetic algorithms is expected to better optimize classification of complex medical data compared to other algorithms.
High Dimensional Biological Data Analysis and Visualization
This document discusses metabolomic data analysis techniques for studying diseases. It analyzes over 13,000 biological samples per year using over 160,000 data points per study. Univariate and multivariate statistical analyses are described, with multivariate being preferred. Techniques include principal component analysis, partial least squares discriminant analysis, hierarchical clustering analysis, and pathway enrichment analysis. Visualization and network mapping tools are also discussed to identify relationships between altered metabolites and treatment effects.
molecular docking
The document discusses docking, which predicts the optimal binding configuration between two molecules by optimizing their orientation and interaction energy. It describes protein-protein docking where both molecules are rigid, and protein-ligand docking where the ligand is flexible but the protein is rigid. It also discusses the AutoDock software, which uses grids and heuristic search algorithms like genetic algorithms to model docking. It provides examples of docking interleukin-10 to an alkaloid ligand, and nuclear factor kappa-B to a ligand. The document advises considering compounds with binding energies close to or better than a positive control as potential hits.
Machine learning in computational docking
The document summarizes recent developments in using machine learning techniques for computational drug docking. It finds that machine learning methods, such as random forests, can more accurately predict binding affinity between proteins and ligands compared to traditional scoring functions. Specifically, the best random forest model achieved a correlation of 0.803 between predicted and experimental binding affinity, compared to 0.644 for classical scoring functions. Machine learning also more accurately ranks ligands and identifies the top binding pose. The document concludes that machine learning is better able to utilize relevant molecular features for computational drug docking compared to traditional methods.
5 data analysis case study
This document summarizes an analysis comparing the primary leaf metabolites of pumpkin and tomatillo plants. The goal was to carry out statistical analyses, hierarchical cluster analysis (HCA), principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (O-PLS-DA) on metabolite profile data from pumpkin and tomatillo leaf samples. Both the HCA and PCA suggested that the treatment effect on metabolite profiles was minor compared to differences between species. A PLS-DA model was validated and found to have outstanding performance in discriminating between pumpkin and tomatillo leaf metabolites. Top discriminating metabolites between the species were then identified.
Omic Data Integration Strategies
Discussion of 'Omic' (e.g. genomic, transcriptomic, proteomic and metabolimic) data integration approaches including:
Gene ontology (GO) enrichment
Genes + Metabolites functional enrichment
Gene + protein + metabolite network mapping
Metabolomic data analysis and visualization tools
This document discusses tools and methods for metabolomic data analysis and visualization. It covers visualization techniques like plots and networks to explore patterns in data. It also discusses statistical analysis methods like ANOVA and clustering for significance testing and pattern detection. Additionally, it discusses predictive modeling, network analysis using pathways, and network mapping to relate metabolites based on biochemical transformations, structural similarity, or empirical dependencies. Common analysis tasks and featured open-source tools are also highlighted.
MOLECULAR DOCKING
Molecular docking is the process of placing molecules in configurations to interact with a receptor. There are two main types - rigid docking treats the receptor and ligand as rigid, while flexible docking allows rotation of one molecule. The key stages of molecular docking are receptor and ligand preparation, docking, and evaluation to identify ligands that bind well and predict binding affinity.
Data analysis workflows part 1 2015
part 2 https://github.com/dgrapov/TeachingDemos/blob/master/Demos/Data%20Analysis%20Workflow/report/report.md
Determining stable ligand orientation
Each and every biological function in living organism occurs due to protein-protein interactions. The
diseases are no exception to this. Identifying one or more proteins for a particular disease and then
designing a suitable chemical compound (which is known as drug or ligand) to destroy those proteins is a
challenging topic of research in computational biology. In earlier methods, drugs were designed using only
a few chemical components and were represented as a fixed-length tree. But in reality, a drug contains
many chemical groups collectively known as pharmacophore. Moreover, the chemical length of the drug
cannot be determined before designing that drug.
In the present work, a Particle Swarm Optimization (PSO) based methodology has been proposed to find
out a suitable drug for a particular disease so that the drug-target protein interaction energy becomes
minimum. In the proposed algorithm, the drug is represented as a variable length tree and essential
functional groups are arranged in different positions of that drug. Finally, the structure of the drug is
obtained and its docking energy is minimized simultaneously. Also, the orientation of chemical groups in
the drug is tested so that it can bind to a particular active site of a target protein and the drug fits well
inside the active site of target protein. Here, several inter-molecular forces have been considered for
accuracy of the docking energy. Results are demonstrated for three different target proteins both
numerically and pictorially. Results show that PSO performs better than the earlier methods.
Managing Virtual Teams
The document provides 10 tips for best practices in managing virtual teams using web tools. It recommends establishing clear standards and communication routines, selecting the right collaboration tools, and finding ways for team members to socially interact and share ideas online. It also emphasizes celebrating accomplishments, creating transparency, enabling ongoing learning, and considering the diversity of team members when selecting tools. The overall goal is to effectively manage remote teams through a mix of traditional and new web-based technologies.
Managing in virtual environment . Part 1. Becoming virtual.
Managing in virtual environment . Part 1. Becoming virtual.Siedlce University of Natural Sciences and Humanities
This document discusses managing in a virtual environment. It begins with an overview of becoming virtual and the influence of the digital world on life and business. It then outlines 10 topics that will be covered in the course, including virtual forms of work organization, communication in virtual teams, and customer relationship management in a virtual environment. The document goes on to discuss the internet and its revolutionary impact on communication, education, business, and personal relationships. It provides examples of virtual worlds like Second Life and how they function as social networking and business platforms.More Related Content
What's hot
Day2_Plenary_Peter R Steward
Day2_Plenary_Peter R Steward CCAFS | CGIAR Research Program on Climate Change, Agriculture and Food Security
Presentation at the 5th Global Science Conference on Climate-Smart Agriculture.
Title: Synergy or Antagonism: Interactions among CSA practices in Africa
Speaker: Peter R Steward 3 principal components analysis
This document discusses using principal component analysis (PCA) to analyze metabolomic sample data from pumpkin experiments. PCA was performed on the raw data and scaled data to identify major sources of variance. For the raw data, the first two principal components captured most of the variance and separated samples by extraction method and treatment. Several samples were identified as potential outliers. When PCA was done on autoscaled data, the loadings showed differences due to both extraction and treatment. The scaled analysis also identified some outlier samples.
Data analysis workflows part 2 2015
previous: https://github.com/dgrapov/TeachingDemos/blob/master/Demos/Data%20Analysis%20Workflow/report/report.md
Connecting Metabolomic Data with Context
The document summarizes Dmitry Grapov's presentation on connecting metabolomic data with context. It discusses using network mapping and multivariate tools to analyze metabolomic data by generating connections between metabolites based on biochemical, chemical, and empirical relationships. These connections can help identify relationships between experimental observations and link the known with unknown. The presentation also provides examples of projects applying these techniques to analyze data from various disease studies involving changes in lipids, proteins, and small molecule metabolites.
Ligand efficiency: nice concept shame about the metrics
Ligand efficiency metrics are meant to normalize ligand activity with respect to molecular properties that increase risk, however they make assumptions about relationships that are not always valid. Residuals, which quantify the extent to which activity beats a trend line fit to the data, may be a better approach as they do not depend on standard concentration units or require defining a trend line a priori. Normalizing data requires accurately modeling the observed trends in a data set, and ligand efficiency metrics can distort perception if the trends are not properly considered.
Metabolomic Data Analysis Workshop and Tutorials (2014)
This document provides an introduction and overview of tutorials for metabolomic data analysis. It discusses downloading required files and software. The goals of the analysis include using statistical and multivariate analyses to identify differences between sample groups and impacted biochemical domains. It also discusses various data analysis techniques including data quality assessment, univariate and multivariate statistical analyses, clustering, principal component analysis, partial least squares modeling, functional enrichment analysis, and network mapping.
Accelerating lead optimisation with active learning by exploiting MMPA based ...
Presented at the 15th GCC - German Conference on Cheminformatics November 2019
We combine regression forest machine learning with our MMPA based generative methods to deliver an active learning system to accelerate lead optimisation. In the process we identify permutative MMPA as a method to leverage SAR information from small data sets.
Published by MedChemica Ltd
Data analysis and Visualisation Techniques for Compound Combination Modelling
A talk given at the EBI for the Cambridge Cheminformatics Network Meeting on the 25/11/2015, introducing compound combination modelling analysis and visualisation techniques.
Metabolomics and Beyond Challenges and Strategies for Next-gen Omic Analyses
Dr. Dmitry Grapov gave a webinar on challenges and strategies for next-generation omics analyses. He discussed how large, longitudinal studies integrating multiple omics domains are needed to identify small biological effects. Data normalization strategies must be considered during experimental design to remove analytical batch effects. Quality control-based normalization using analytical replicates can estimate and remove analytical variance from large datasets. Integrating multiple measurement platforms is often required to identify systems of biological changes. Network-based analysis of omics data can help explain more phenotypic variance than single omics approaches alone. Dr. Grapov demonstrated software tools he developed for network analysis, visualization, and integration of multi-omics datasets.
2 cluster analysis
The document discusses using hierarchical cluster analysis (HCA) to evaluate metabolomic sample processing methods. It describes two goals: 1) Use HCA to cluster samples based on raw data similarities and correlations to determine the impact of extraction and treatment methods on data variance. Extraction had the greatest effect, with ACN:/IPA/water and MeOH/CH3Cl/water samples most similar. 2) Use HCA to cluster metabolites based on z-scaled data and correlations to identify groups of related metabolites and evaluate the robustness of different correlation measures. Clusters extracted from the correlation-based dendrogram contained metabolites that shared biological functions.
IRJET- Classification of Chemical Medicine or Drug using K Nearest Neighb...
This document proposes using a combination of K-nearest neighbors (KNN) and genetic algorithms to classify chemical medicine or drug data with improved accuracy. KNN is described as a simple and effective classification algorithm that stores training data instances. Genetic algorithms are presented as evolutionary algorithms useful for optimization problems. The proposed system applies genetic search to rank attribute importance, selects high-ranked attributes, and then applies both KNN and genetic algorithms to classify the drug data, aiming to improve classification accuracy over using either technique alone. The combination of KNN and genetic algorithms is expected to better optimize classification of complex medical data compared to other algorithms.
High Dimensional Biological Data Analysis and Visualization
This document discusses metabolomic data analysis techniques for studying diseases. It analyzes over 13,000 biological samples per year using over 160,000 data points per study. Univariate and multivariate statistical analyses are described, with multivariate being preferred. Techniques include principal component analysis, partial least squares discriminant analysis, hierarchical clustering analysis, and pathway enrichment analysis. Visualization and network mapping tools are also discussed to identify relationships between altered metabolites and treatment effects.
molecular docking
The document discusses docking, which predicts the optimal binding configuration between two molecules by optimizing their orientation and interaction energy. It describes protein-protein docking where both molecules are rigid, and protein-ligand docking where the ligand is flexible but the protein is rigid. It also discusses the AutoDock software, which uses grids and heuristic search algorithms like genetic algorithms to model docking. It provides examples of docking interleukin-10 to an alkaloid ligand, and nuclear factor kappa-B to a ligand. The document advises considering compounds with binding energies close to or better than a positive control as potential hits.
Machine learning in computational docking
The document summarizes recent developments in using machine learning techniques for computational drug docking. It finds that machine learning methods, such as random forests, can more accurately predict binding affinity between proteins and ligands compared to traditional scoring functions. Specifically, the best random forest model achieved a correlation of 0.803 between predicted and experimental binding affinity, compared to 0.644 for classical scoring functions. Machine learning also more accurately ranks ligands and identifies the top binding pose. The document concludes that machine learning is better able to utilize relevant molecular features for computational drug docking compared to traditional methods.
5 data analysis case study
This document summarizes an analysis comparing the primary leaf metabolites of pumpkin and tomatillo plants. The goal was to carry out statistical analyses, hierarchical cluster analysis (HCA), principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (O-PLS-DA) on metabolite profile data from pumpkin and tomatillo leaf samples. Both the HCA and PCA suggested that the treatment effect on metabolite profiles was minor compared to differences between species. A PLS-DA model was validated and found to have outstanding performance in discriminating between pumpkin and tomatillo leaf metabolites. Top discriminating metabolites between the species were then identified.
Omic Data Integration Strategies
Discussion of 'Omic' (e.g. genomic, transcriptomic, proteomic and metabolimic) data integration approaches including:
Gene ontology (GO) enrichment
Genes + Metabolites functional enrichment
Gene + protein + metabolite network mapping
Metabolomic data analysis and visualization tools
This document discusses tools and methods for metabolomic data analysis and visualization. It covers visualization techniques like plots and networks to explore patterns in data. It also discusses statistical analysis methods like ANOVA and clustering for significance testing and pattern detection. Additionally, it discusses predictive modeling, network analysis using pathways, and network mapping to relate metabolites based on biochemical transformations, structural similarity, or empirical dependencies. Common analysis tasks and featured open-source tools are also highlighted.
MOLECULAR DOCKING
Molecular docking is the process of placing molecules in configurations to interact with a receptor. There are two main types - rigid docking treats the receptor and ligand as rigid, while flexible docking allows rotation of one molecule. The key stages of molecular docking are receptor and ligand preparation, docking, and evaluation to identify ligands that bind well and predict binding affinity.
Data analysis workflows part 1 2015
part 2 https://github.com/dgrapov/TeachingDemos/blob/master/Demos/Data%20Analysis%20Workflow/report/report.md
Determining stable ligand orientation
Each and every biological function in living organism occurs due to protein-protein interactions. The
diseases are no exception to this. Identifying one or more proteins for a particular disease and then
designing a suitable chemical compound (which is known as drug or ligand) to destroy those proteins is a
challenging topic of research in computational biology. In earlier methods, drugs were designed using only
a few chemical components and were represented as a fixed-length tree. But in reality, a drug contains
many chemical groups collectively known as pharmacophore. Moreover, the chemical length of the drug
cannot be determined before designing that drug.
In the present work, a Particle Swarm Optimization (PSO) based methodology has been proposed to find
out a suitable drug for a particular disease so that the drug-target protein interaction energy becomes
minimum. In the proposed algorithm, the drug is represented as a variable length tree and essential
functional groups are arranged in different positions of that drug. Finally, the structure of the drug is
obtained and its docking energy is minimized simultaneously. Also, the orientation of chemical groups in
the drug is tested so that it can bind to a particular active site of a target protein and the drug fits well
inside the active site of target protein. Here, several inter-molecular forces have been considered for
accuracy of the docking energy. Results are demonstrated for three different target proteins both
numerically and pictorially. Results show that PSO performs better than the earlier methods.
What's hot (20)
Ligand efficiency: nice concept shame about the metrics
Ligand efficiency: nice concept shame about the metrics
Metabolomic Data Analysis Workshop and Tutorials (2014)
Metabolomic Data Analysis Workshop and Tutorials (2014)
Accelerating lead optimisation with active learning by exploiting MMPA based ...
Accelerating lead optimisation with active learning by exploiting MMPA based ...
Data analysis and Visualisation Techniques for Compound Combination Modelling
Data analysis and Visualisation Techniques for Compound Combination Modelling
Metabolomics and Beyond Challenges and Strategies for Next-gen Omic Analyses
Metabolomics and Beyond Challenges and Strategies for Next-gen Omic Analyses
IRJET- Classification of Chemical Medicine or Drug using K Nearest Neighb...
IRJET- Classification of Chemical Medicine or Drug using K Nearest Neighb...
High Dimensional Biological Data Analysis and Visualization
High Dimensional Biological Data Analysis and Visualization
Viewers also liked
Managing Virtual Teams
The document provides 10 tips for best practices in managing virtual teams using web tools. It recommends establishing clear standards and communication routines, selecting the right collaboration tools, and finding ways for team members to socially interact and share ideas online. It also emphasizes celebrating accomplishments, creating transparency, enabling ongoing learning, and considering the diversity of team members when selecting tools. The overall goal is to effectively manage remote teams through a mix of traditional and new web-based technologies.
Managing in virtual environment . Part 1. Becoming virtual.
Managing in virtual environment . Part 1. Becoming virtual.Siedlce University of Natural Sciences and Humanities
This document discusses managing in a virtual environment. It begins with an overview of becoming virtual and the influence of the digital world on life and business. It then outlines 10 topics that will be covered in the course, including virtual forms of work organization, communication in virtual teams, and customer relationship management in a virtual environment. The document goes on to discuss the internet and its revolutionary impact on communication, education, business, and personal relationships. It provides examples of virtual worlds like Second Life and how they function as social networking and business platforms.Mc Kirkley Oral Defense 11122009 V2
This document provides an outline for a qualitative phenomenological study exploring how experienced managers adapt their leadership strategies and management practices to support telework and virtual employees. The study involved interviews with 14 managers from technology organizations. Key findings included that while technological advances enhanced communication, organizations did not provide sufficient guidance or training for managing virtual teams. Managers had to learn skills like improved communication and multitasking on their own. The study confirmed literature showing a lack of clear virtual processes and that training is needed for virtual organizations to be efficient.
Managing Virtual Teams
1. The document discusses managing virtual teams, which are comprised of people across different locations who use technology to collaborate remotely. It outlines five principles for managing virtual teams effectively: manage proactively, respect and trust team members, manage consistently with discipline, set realistic expectations and hold teams accountable, and foster fun.
2. Trends show that mobile technology has changed where and how people work, and virtual teams can work seamlessly with the right collaboration tools. However, managing virtual teams presents challenges in aligning culture, performance, communication and technology.
3. Proactive management is important for virtual teams, with open communication and coaching to build cohesion. Trust, clear expectations, accountability, and making meetings effective are
Matrix Structure Navigating & Making It Work
The document discusses navigating matrix organizations and making them work effectively. It begins with an overview of different types of organizational structures, including centralized functional, multi-divisional, and matrix structures. The matrix structure aims to achieve the benefits of both functional expertise and divisional market focus. However, collaboration does not happen automatically and requires explicit strategies, structures, and systems. Potential sources of collaborative value are then outlined, including financial, customer, process, and learning & growth synergies across business units. Overall, the document argues that matrix organizations can enhance competitive advantage by optimizing both global/group and local tradeoffs, but it requires collaborative strategies, structures, and systems developed by the corporate center.
Managing virtual teams
What is a virtual team?
What are the advantages?
What are the challenges?
How can we overcome them?
TMA World Viewpoint 26: Matrix Organization Best Practices - Extending Your C...
The matrix structure is a well established organizational mechanism for managing people, products and projects across multiple geographies.
Effective leadership in a matrix environment requires us to achieve a balance between exercising direct leadership authority whilst simultaneously conveying indirect leadership influence across organizational boundaries.
This TMA World presentation explores eight practical approaches you can role model as a leader to support the effectiveness of your people working in a matrix environment.
For more advice on how to thrive in the borderless workplace, including working in complex, global environments, contact us today: enquiries@tmaworld.com
Surviving in a Matrix: Simple Techniques for Effective Project Management in ...
The document outlines strategies for effective project management in a matrix organization. It discusses establishing (1) clear project objectives, structure, and organization, (2) monitoring project status through an early warning system of regular status updates, and (3) ongoing learning through lessons learned sessions. The presentation provides examples for work breakdown structures, defining team roles and responsibilities, tracking plan deviations, and prioritizing feedback for improvement. The goal is to facilitate project success through planning, coordination, and continuous adaptation in a matrix structure where resources are shared among multiple teams.
Matrix management
An overview of matrix management, by Kevan Hall, CEO of Global Integration and author of "Making the Matrix Work"
Kim Kardashian Fleet Media
Five Star increased sales by 10% on past campaigns, just by using Fleet Media. Imagine what this 200 semi-trailers with this campaign is going to do!
كيف يعامل المسنون في دين الإسلام
كيف يعامل المسنون في دين الإسلام
Islam Question and Answer_Q&A
http://www.4shared.com/dir/15178918/ba3d9a0e/Islam_Question_and_Answer_QA.html
толкование десяти последних джузов благородного корана, и следует за ним Last...
толкование десяти последних джузов благородного корана, и следует за ним Last Tenth Russian
http://www.listen2quran.com/
The Anti Islam Dutch Film – Comment, Criticism And Advice
The document provides commentary and advice on an anti-Islam Dutch film. It discusses:
1) The film maligns Islam through misquotations, fabricated images, and out-of-context clips in an attempt to falsely link Islam to terrorism.
2) The film's attempts to insult Islam have backfired, increasing interest in learning about the true teachings of Islam.
3) Many non-Muslim groups and leaders have condemned the film for promoting hate and being factually incorrect in its portrayal of Islam.
Disability Insurance
Disability insurance provides income benefits if a person is unable to work due to illness or injury. There are two main types: short-term disability has a maximum benefit period of up to two years, while long-term disability has a maximum period ranging from a few years to life. Coverage can be for inability to work in your own occupation or any occupation. Most plans do not cover disabilities from drug abuse, suicide attempts, war or crimes. Protection features include policies being noncancelable or guaranteed renewable.
401 K
When starting a new job, employees should inquire about the company's 401(k) plan from human resources or knowledgeable managers to determine if they are eligible. If eligible, 401(k)s allow contributions of up to 15% of an employee's paycheck, with companies providing matching contributions. For those ineligible for a 401(k), alternatives include Roth IRAs, traditional IRAs, pensions, CDs or bonds. Roth IRAs offer tax-free growth of principal and fewer restrictions on withdrawals than other options.
رسالة الى الباحثين عن الحقيقة
May I suggest to the seeker of truth do the following NINE STEPS for purification of the mind:
Clean their mind, their heart and their soul real good.
Clear away all the prejudices and biases.
Read a good translation of the meaning of the Holy Quran in a language that they can understand best.
Take some time.
Read and reflect.
Think and pray.
And keep on asking the One who created you in the first place, to guide you to the truth.
Keep this up for a few months. And be regular in it.
Above all, do not let others who are poisoned in their thinking influence you while your are in this state of "rebirth of the soul."
The rest is between you and the Almighty Lord of the Universe. If you truly love Him, then He already knows it and He will deal with each of us according to our hearts.
Peace to you and Guidance from Allah the One Almighty God, Creator and Sustainer of all that exists.
==========================
I am pleased to invite you to:
http://www.quranexplorer.com/quran/ http://www.islamreligion.com , http://quranicaudio.com, http://www.islamtomorrow.com/, http://www.islamhouse.com/, http://www.islam-guide.com/ , http://www.harunya
See you soon,
Abdullah Baspren
Ihoughton Strengthening Accountability Against Multi Lateral Agreements V031011
The document discusses the expanding influence of African multilateral institutions like the African Union, and citizens' efforts to hold governments accountable to progressive human rights standards. It provides two case studies: 1) The State of the African Union Coalition, a coalition of 10 CSOs that publishes biannual reports on government compliance with AU policies and advocates for citizens' freedoms. This has led the AU to request expedited ratification of treaties. 2) A Malawi campaign lobbying MPs and monitoring dispensaries that increased health budget allocation from 8% to 21% of the total budget. The document argues citizens are increasingly claiming rights enshrined in AU instruments to influence governance.
Muongozo Wa Kuufahamu Uislamu Kwa Ufupi Na Kwa Kutumia Vielelezo Vya Picha _ ...
Muongozo Wa Kuufahamu Uislamu Kwa Ufupi Na Kwa Kutumia Vielelezo Vya Picha_ Sawahili
Viewers also liked (20)
Managing in virtual environment . Part 1. Becoming virtual.
Managing in virtual environment . Part 1. Becoming virtual.
TMA World Viewpoint 26: Matrix Organization Best Practices - Extending Your C...
TMA World Viewpoint 26: Matrix Organization Best Practices - Extending Your C...
Surviving in a Matrix: Simple Techniques for Effective Project Management in ...
Surviving in a Matrix: Simple Techniques for Effective Project Management in ...
толкование десяти последних джузов благородного корана, и следует за ним Last...
толкование десяти последних джузов благородного корана, и следует за ним Last...
The Anti Islam Dutch Film – Comment, Criticism And Advice
The Anti Islam Dutch Film – Comment, Criticism And Advice
Ihoughton Strengthening Accountability Against Multi Lateral Agreements V031011
Ihoughton Strengthening Accountability Against Multi Lateral Agreements V031011
Muongozo Wa Kuufahamu Uislamu Kwa Ufupi Na Kwa Kutumia Vielelezo Vya Picha _ ...
Muongozo Wa Kuufahamu Uislamu Kwa Ufupi Na Kwa Kutumia Vielelezo Vya Picha _ ...
Similar to Data Mining Using a Consensus Algorithm
Deep learning methods applied to physicochemical and toxicological endpoints
Chemical and pharmaceutical companies, and government agencies regulating both chemical and biological compounds, all strive to develop new methods to provide efficient prioritization, evaluation and safety assessments for the hundreds of new chemicals that enter the market annually. While there is a lot of historical data available within the various agencies, organizations and companies, significant gaps remain in both the quantity and quality of data available coupled with optimal predictive methods. Traditional QSAR methods are based on sets of features (fingerprints) which representing the functional characteristics of chemicals. Unfortunately, due to both data gaps and limitations in the development of QSAR models, read-across approaches have become a popular area of research. Successes in the application of Artificial Neural Networks, and specifically in Deep Learning Neural Networks, has delivered a new optimism that the lack of data and limited feature sets can be overcome by using Deep Learning methods. In this poster we will present a comparison of various machine learning methods applied to several toxicological and physicochemical parameter endpoints. This abstract does not reflect U.S. EPA policy.
2007 Pharmasug, Promotion Response Analysis
This document summarizes various methods for measuring the return on investment of promotional events in the pharmaceutical industry, including test and control matching. It discusses propensity score matching, Mahalanobis distance matching, and caliper matching. Specifically, it provides a useful historical reference on caliper matching from Cochran and Rubin regarding how the variance of the matching variable affects bias reduction. It also explains how Mahalanobis distance matching incorporates correlations between variables to identify observations that lie far from the data center.
Hc3413121317
International Journal of Engineering Research and Applications (IJERA) is a team of researchers not publication services or private publications running the journals for monetary benefits, we are association of scientists and academia who focus only on supporting authors who want to publish their work. The articles published in our journal can be accessed online, all the articles will be archived for real time access.
Our journal system primarily aims to bring out the research talent and the works done by sciaentists, academia, engineers, practitioners, scholars, post graduate students of engineering and science. This journal aims to cover the scientific research in a broader sense and not publishing a niche area of research facilitating researchers from various verticals to publish their papers. It is also aimed to provide a platform for the researchers to publish in a shorter of time, enabling them to continue further All articles published are freely available to scientific researchers in the Government agencies,educators and the general public. We are taking serious efforts to promote our journal across the globe in various ways, we are sure that our journal will act as a scientific platform for all researchers to publish their works online.
Robust Breast Cancer Diagnosis on Four Different Datasets Using Multi-Classif...
Abstract- The goal of this paper is to compare between different classifiers or multi-classifiers fusion with respect to accuracy in discovering breast cancer for four different data sets. We present an implementation among various classification techniques which represent the most known algorithms in this field on four different datasets of breast cancer two for diagnosis and two for prognosis. We present a fusion between classifiers to get the best multi-classifier fusion approach to each data set individually. By using confusion matrix to get classification accuracy which built in 10-fold cross validation technique. Also, using fusion majority voting (the mode of the classifier output). The experimental results show that no classification technique is better than the other if used for all datasets, since the classification task is affected by the type of dataset. By using multi-classifiers fusion the results show that accuracy improved in three datasets out of four.
ENHANCED BREAST CANCER RECOGNITION BASED ON ROTATION FOREST FEATURE SELECTIO...
Optimization problems are dominantly being solved using Computational Intelligence. One of
the issues that can be addressed in this context is problems related to attribute subset selection
evaluation. This paper presents a computational intelligence technique for solving the
optimization problem using a proposed model called Modified Genetic Search Algorithms
(MGSA) that avoids local bad search space with merit and scaled fitness variables, detecting
and deleting bad candidate chromosomes, thereby reducing the number of individual
chromosomes from search space and subsequent iterations in next generations. This paper aims
to show that Rotation forest ensembles are useful in the feature selection method. The base
classifier is multinomial logistic regression method integrated with Haar wavelets as projection
filter and reproducing the ranks of each features with 10 fold cross validation method. It also
discusses the main findings and concludes with promising result of the proposed model. It
explores the combination of MGSA for optimization with Naïve Bayes classification. The result
obtained using proposed model MGSA is validated mathematically using Principal Component
Analysis. The goal is to improve the accuracy and quality of diagnosis of Breast cancer disease
with robust machine learning algorithms. As compared to other works in literature survey,
experimental results achieved in this paper show better results with statistical inferenc
An experimental study on hypothyroid using rotation forest
This paper majorly focuses on hypothyroid medical diseases caused by underactive thyroid glands. The
dataset used for the study on hypothyroid is taken from UCI repository. Classification of this thyroid
disease is a considerable task. An experimental study is carried out using rotation forest using features
selection methods to achieve better accuracy. An important step to gain good accuracy is a pre- processing
step, thus here two feature selection techniques are used. A filter method, Correlation features subset
selection and wrappers method has helped in removing irrelevant as well as useless features from the data
set. Fourteen different machine learning algorithms were tested on hypothyroid data set using rotation
forest which successfully turned out giving positively improved results
[IJET-V2I3P21] Authors: Amit Kumar Dewangan, Akhilesh Kumar Shrivas, Prem Kumar
[IJET-V2I3P21] Authors: Amit Kumar Dewangan, Akhilesh Kumar Shrivas, Prem KumarIJET - International Journal of Engineering and Techniques
sis of health condition is very challenging task for every human being because life is directly related to health
condition. Data mining based classification is one of the important applications for classification of data. In this
research work, we have used various classification techniques for classification of thyroid data. CART gives highest
accuracy 99.47% as best model. Feature selection plays very important role to computationally efficient and increase
the performance of model. This research work focus on Info Gain and Gain Ratio feature selection technique to
reduce the irrelevant features from original data set and computationally increase the performance of model. We have
applied both the feature selection techniques on best model i. e. CART. Our proposed CART-Info Gain and CARTGain
Ratio gives 99.47% and 99.20% accuracy with 25 and 3 feature respectively.Seminar Slides
The document discusses various feature subset selection methods for gene expression datasets, which have a large number of attributes and small number of samples. It describes filter methods like rank-based and space search-based approaches, as well as wrapper and embedded methods. Rank-based filters calculate correlations like Pearson and mutual information scores to select relevant features. Space search filters evaluate feature subsets for relevancy and redundancy. The document also discusses unsupervised feature selection using maximal information coefficient and affinity propagation clustering. It provides an example of applying feature selection to breast cancer subtyping using consensus clustering across multiple datasets.
CAD v2
This document discusses computer aided detection (CAD) of abnormalities in medical images. It begins by outlining CAD and some of the key machine learning challenges, including correlated training data, non-standard evaluation metrics, runtime constraints, lack of objective ground truths, and data shortages. It then describes solutions like multiple instance learning, batch classification, cascaded classifiers, crowdsourcing algorithms, and multi-task learning. The document concludes by reviewing the clinical impact of CAD systems through several independent studies, which demonstrated improved radiologist performance and sensitivity in detecting diseases.
A Threshold Fuzzy Entropy Based Feature Selection: Comparative Study
Feature selection is one of the most common and critical tasks in database classification. It
reduces the computational cost by removing insignificant and unwanted features. Consequently, this
makes the diagnosis process accurate and comprehensible. This paper presents the measurement of
feature relevance based on fuzzy entropy, tested with Radial Basis Classifier (RBF) network,
Bagging(Bootstrap Aggregating), Boosting and stacking for various fields of datasets. Twenty
benchmarked datasets which are available in UCI Machine Learning Repository and KDD have been
used for this work. The accuracy obtained from these classification process shows that the proposed
method is capable of producing good and accurate results with fewer features than the original
datasets.
Virtual screening of chemicals for endocrine disrupting activity through CER...
Endocrine disrupting chemicals (EDCs) are xenobiotics that mimic the interaction of natural hormones at the receptor level and alter synthesis, transport and metabolism pathways. The prospect of EDCs causing adverse health effects in humans and wildlife has led to the development of scientific and regulatory approaches for evaluating bioactivity. This need is being partially addressed by the use of high-throughput screening (HTS) in vitro approaches and computational modeling. In the framework of the Endocrine Disruptor Screening Program (EDSP), the U.S. EPA led two worldwide consortiums to “virtually” (i.e., in silico) screen chemicals for their potential estrogenic and androgenic activities. The Collaborative Estrogen Receptor (ER) Activity Prediction Project (CERAPP) [1] predicted activities for 32,464 chemicals and the Collaborative Modeling Project for Androgen Receptor (AR) Activity (CoMPARA) generated predictions on the CERAPP list with additional simulated metabolites, totaling 55,450 unique structures. Modelers and computational toxicology scientists from 30 international groups contributed structure-based models and results for activity prediction to one or both projects, with methods ranging from QSARs to docking to predict binding, agonism and antagonism activities. Models were based on a common training set of 1746 chemicals having ToxCast/Tox21 HTS in vitro assay results (18 assays for ER and 11 for AR) integrated into computational networks. The models were then validated using curated literature data from different sources (~7,000 results for ER and ~5,000 results for AR). To overcome the limitations of single approaches, CERAPP and CoMPARA models were each combined into consensus models reaching high predictive accuracy. These consensus models were extended beyond the initially designed datasets by implementing them into the free and open-source application OPERA to avoid running every single model on new chemicals [2]. This implementation was used to screen the entire EPA DSSTox database of ~750,000 chemicals and predicted ER and AR activity is made freely available on the CompTox Chemistry dashboard (https://comptox.epa.gov/dashboard) [3].
Classification of Breast Cancer Tissues using Decision Tree Algorithms
Classification of Breast Cancer Tissues using Decision Tree AlgorithmsLovely Professional University
Nowadays, Healthcare sector data are enormous, composite and diverse because it contains a data of different types and getting knowledge from that data is essential. So for this purpose, data mining techniques may be utilized to mine knowledge by building models from healthcare dataset. At present, the classification of breast cancer patients has been a demanding research confront for many researchers. For building a classification model for the cancer patient, we used four different classification algorithms such as J48, REPTree, RandomForest, and RandomTree and tested on the dataset taken from UCI. The main aim of this paper is to classify the patient into benign (not cancer) or malignant (cancer), based on some diagnostic measurements integrated into the dataset.Welcome to International Journal of Engineering Research and Development (IJERD)
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
In-silico structure activity relationship study of toxicity endpoints by QSAR...
Several thousand chemicals were tested in hundreds of toxicity-related in-vitro high-throughput screening (HTS)
bioassays through the EPA’s ToxCast and Tox21 projects. However, this chemical set only covers a portion of the chemical
space of interest for environmental risk assessment, leading to a need to fill data gaps with other methods. A cost effective
and reliable approach to fullfill this task is to build quantitative structure-activity relationships (QSARs).
In this work, a subset of 1877 chemicals from ToxCast were used to build QSAR models. These models will be applied
to predict values for multiple ToxCast assays in a larger environmental database of ~30K chemical structures.
Based on a clustering study by Sipes et al. (2013), the initial molecular targets of this effort consisted of a set of 18
NovaScreen G-protein coupled receptor (GPCR) assays. These assays are part of the aminergic category that showed the
highest number of actives within the ToxCast portfolio. Classification methods including SOM, SVM, PLSDA and kNN, were
tested. These methods were coupled to variable selection techniques such as genetic algorithms that were applied in order
to select the best representative molecular descriptors based on statistical fitness functions. The obtained models were
validated and their prediction ability measured. The models that showed good results will be applied within the limits of
their established chemical space defined by the applicability domain.
Advance KNN classification of brain tumor
The document discusses using random forests and KNN algorithms to segment and classify brain tumors. It proposes using selective search segmentation to extract important regions from images before applying random forests, in order to address issues with random patch selection. The goals are to increase discriminative power and accuracy of the random forest classifier by selecting more informative image patches from the segmentation. Literature on using decision tree ensembles for biomarker identification and classification is also reviewed. Performance is measured using metrics like accuracy, ROC curves, and error rates.
How predictive models help Medicinal Chemists design better drugs_webinar
All scientific disciplines, including medicinal chemistry, are experiencing a revolution in unprecedented rates of data being generated and the subsequent analysis and exploitation of this data is increasingly fundamental to innovation. Using data to design better compounds is a challenge for Medicinal and Computational chemists.
The design of small-molecule drug candidates, encompassing characteristics such as potency, selectivity and ADMET (absorption, distribution, metabolism, excretion and toxicity) is a key factor in the success of clinical trials and computer-aided drug discovery/design methods have played a major role in the development of therapeutically important small molecules for over three decades. These methods are broadly classified as either structure-based or ligand-based.
In this webinar our expert Dr. Olivier Barberan will discuss ligand-based methods and he will cover the following:
How to use only ligand information to predict activity depending on its similarity/dissimilarity to previously known active ligands.
- Discuss ligand-based pharmacophores, molecular descriptors, and quantitative structure-activity relationships and important tools such as target/ligand databases necessary for successful implementation of various computer-aided drug discovery/design methods in a drug discovery campaign.
NMR, deep learning and molecular structure: a call for data
Poster (Presented at GCC2017 Mainz) describing initial studies using neural networks to identify chemical functional groups by examining carbon-13 NMR spectra.
Controlling informative features for improved accuracy and faster predictions...
Controlling informative features for improved accuracy and faster predictions...Damian R. Mingle, MBA
Identification of suitable biomarkers for accurate prediction of phenotypic outcomes is a goal for personalized medicine. However, current machine learning approaches are either too complex or perform poorly.
For more information:
http://societyofdatascientists.com/controlling-informative-features-for-improved-accuracy-and-faster-predictions-in-omentum-cancer-models/?src=slideshare
Robust Breast Cancer Diagnosis on Four Different Datasets Using Multi-Classif...
The goal of this paper is to compare between different classifiers or multi-classifiers fusion with respect to accuracy in discovering breast cancer for four different data sets. We present an implementation among various classification techniques which represent the most known algorithms in this field on four different datasets of breast cancer two for diagnosis and two for prognosis. We present a fusion between classifiers to get the best multi-classifier fusion approach to each data set individually. By using confusion matrix to get classification accuracy which built in 10-fold cross validation technique. Also, using fusion majority voting (the mode of the classifier output). The experimental results show that no classification technique is better than the other if used for all datasets, since the classification task is affected by the type of dataset. By using multi-classifiers fusion the results show that accuracy improved in three datasets out of four.
PROGRAM TEST DATA GENERATION FOR BRANCH COVERAGE WITH GENETIC ALGORITHM: COMP...
In search based test data generation, the problem of test data generation is reduced to that of
function minimization or maximization.Traditionally, for branch testing, the problem of test data
generation has been formulated as a minimization problem. In this paper we define an alternate
maximization formulation and experimentally compare it with the minimization formulation. We
use a genetic algorithm as the search technique and in addition to the usual genetic algorithm
operators we also employ the path prefix strategy as a branch ordering strategy and memory and elitism. Results indicate that there is no significant difference in the performance or the coverage obtained through the two approaches and either could be used in test data generation when coupled with the path prefix strategy, memory and elitism.
Similar to Data Mining Using a Consensus Algorithm (20)
Deep learning methods applied to physicochemical and toxicological endpoints
Deep learning methods applied to physicochemical and toxicological endpoints
Robust Breast Cancer Diagnosis on Four Different Datasets Using Multi-Classif...
Robust Breast Cancer Diagnosis on Four Different Datasets Using Multi-Classif...
ENHANCED BREAST CANCER RECOGNITION BASED ON ROTATION FOREST FEATURE SELECTIO...
ENHANCED BREAST CANCER RECOGNITION BASED ON ROTATION FOREST FEATURE SELECTIO...
An experimental study on hypothyroid using rotation forest
An experimental study on hypothyroid using rotation forest
[IJET-V2I3P21] Authors: Amit Kumar Dewangan, Akhilesh Kumar Shrivas, Prem Kumar
[IJET-V2I3P21] Authors: Amit Kumar Dewangan, Akhilesh Kumar Shrivas, Prem Kumar
A Threshold Fuzzy Entropy Based Feature Selection: Comparative Study
A Threshold Fuzzy Entropy Based Feature Selection: Comparative Study
Virtual screening of chemicals for endocrine disrupting activity through CER...
Virtual screening of chemicals for endocrine disrupting activity through CER...
Classification of Breast Cancer Tissues using Decision Tree Algorithms
Classification of Breast Cancer Tissues using Decision Tree Algorithms
Welcome to International Journal of Engineering Research and Development (IJERD)
Welcome to International Journal of Engineering Research and Development (IJERD)
In-silico structure activity relationship study of toxicity endpoints by QSAR...
In-silico structure activity relationship study of toxicity endpoints by QSAR...
How predictive models help Medicinal Chemists design better drugs_webinar
How predictive models help Medicinal Chemists design better drugs_webinar
NMR, deep learning and molecular structure: a call for data
NMR, deep learning and molecular structure: a call for data
Controlling informative features for improved accuracy and faster predictions...
Controlling informative features for improved accuracy and faster predictions...
Robust Breast Cancer Diagnosis on Four Different Datasets Using Multi-Classif...
Robust Breast Cancer Diagnosis on Four Different Datasets Using Multi-Classif...
PROGRAM TEST DATA GENERATION FOR BRANCH COVERAGE WITH GENETIC ALGORITHM: COMP...
PROGRAM TEST DATA GENERATION FOR BRANCH COVERAGE WITH GENETIC ALGORITHM: COMP...
Recently uploaded
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
Climate Impact of Software Testing at Nordic Testing Days
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?
ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
20240605 QFM017 Machine Intelligence Reading List May 2024
Everything I found interesting about machines behaving intelligently during May 2024
Presentation of the OECD Artificial Intelligence Review of Germany
Consult the full report at https://www.oecd.org/digital/oecd-artificial-intelligence-review-of-germany-609808d6-en.htm
みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
ここ3000字までしか入らないけどタイトルの方がたくさん文字入ると思います。
How to Get CNIC Information System with Paksim Ga.pptx
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
20240607 QFM018 Elixir Reading List May 2024
Everything I found interesting about the Elixir programming ecosystem in May 2024
Driving Business Innovation: Latest Generative AI Advancements & Success Story
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
Communications Mining Series - Zero to Hero - Session 1
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...
“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...Edge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.Best 20 SEO Techniques To Improve Website Visibility In SERP
Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
UiPath Test Automation using UiPath Test Suite series, part 5
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
“I’m still / I’m still / Chaining from the Block”
“An Outlook of the Ongoing and Future Relationship between Blockchain Technologies and Process-aware Information Systems.” Invited talk at the joint workshop on Blockchain for Information Systems (BC4IS) and Blockchain for Trusted Data Sharing (B4TDS), co-located with with the 36th International Conference on Advanced Information Systems Engineering (CAiSE), 3 June 2024, Limassol, Cyprus.
UiPath Test Automation using UiPath Test Suite series, part 6
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
20240609 QFM020 Irresponsible AI Reading List May 2024
Everything I found interesting about the irresponsible use of machine intelligence in May 2024
Recently uploaded (20)
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...
Climate Impact of Software Testing at Nordic Testing Days
Climate Impact of Software Testing at Nordic Testing Days
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?
20240605 QFM017 Machine Intelligence Reading List May 2024
20240605 QFM017 Machine Intelligence Reading List May 2024
Presentation of the OECD Artificial Intelligence Review of Germany
Presentation of the OECD Artificial Intelligence Review of Germany
みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
How to Get CNIC Information System with Paksim Ga.pptx
How to Get CNIC Information System with Paksim Ga.pptx
Driving Business Innovation: Latest Generative AI Advancements & Success Story
Driving Business Innovation: Latest Generative AI Advancements & Success Story
Communications Mining Series - Zero to Hero - Session 1
Communications Mining Series - Zero to Hero - Session 1
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...
“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...
Best 20 SEO Techniques To Improve Website Visibility In SERP
Best 20 SEO Techniques To Improve Website Visibility In SERP
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
UiPath Test Automation using UiPath Test Suite series, part 5
UiPath Test Automation using UiPath Test Suite series, part 5
UiPath Test Automation using UiPath Test Suite series, part 6
UiPath Test Automation using UiPath Test Suite series, part 6
20240609 QFM020 Irresponsible AI Reading List May 2024
20240609 QFM020 Irresponsible AI Reading List May 2024
Data Mining Using a Consensus Algorithm
- 1. Data Mining Using Consensus Fingerprint Searches AstraZeneca CNS Chemistry, Wilmington, USA * Cancer and Infection Chemistry, Alderley Park, UK ^ James R. Arnold * Charles L. Lerman * William F. Michne * David A. Cosgrove ^ James R. Damewood *
- 3. Fingerprint Based Classification and Data Mining Classification is based on 400 medicinally relevant functional groups. Imigran (1): GSK, 1.07 billion dollar treatment for migraine in 2000.
- 4. * The exclusions make the functional group definitions specific and make the entire set as orthogonal as possible. Pattern Matching Rules
- 5. Classification Quality: Coverage and Overlap of Definitions Ideal Coverage Ideal Overlap Testing in medicinally relevant databases. Roughly 90% coverage and 10% overlap. Coverage : All heteroatoms in molecule are classified. Overlap : A heteroatom in molecule classified in > 1 functional group. CMC = 8,545 MDDR = 135,342 MedCh = 145,158
- 9. The circles are drawn to scale according to the number of actives recovered at given Tanimoto distances. Consensus Approach: Overlap of True Positives from FG Count and Daylight
- 10. Number of true and false positives for the Functional Group Fingerprint counts, Daylight fingerprint and consensus (logical “AND”) approaches for the five hundred and thirty eight biological target classes at Tanimoto distances of 0.0, 0.1, 0.2, and 0.3. The three methods are binned at the various Tanimoto distances and are reported in the order of counts, Daylight, consensus, and are listed as FG, D and C, respectively. Performance of the FG Count, Daylight and Consensus Approaches in Terms of True and False Positives 50% reduction false positives FG = FG Count D = Daylight C = Consensus