Multi-View Design Patterns and Responsive Visualization for Genomics Data.ppt
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Few-Shot learning for enhancing dataset cleaning:A competitive study with SVM and CNN clustering model
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This document describes a proposed hybrid technique for automatic medical image classification and retrieval using information retrieval, support vector machines, and particle swarm optimization. Key aspects of the proposed approach include extracting low-level visual features from images like color, texture, shape and integrating them with semantic metadata. A content analysis system analyzes image descriptors and assigns semantic labels. Images are indexed and classified during a training phase. The proposed system aims to reduce the semantic gap between low-level features and high-level semantics by combining content-based image retrieval with text-based retrieval and machine learning algorithms.
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Genome-wide transcription profiling is a powerful technique in studying disease susceptible footprints. Moreover, when applied to disease tissue it may reveal quantitative and qualitative alterations in gene expression that give information on the context or underlying basis for the disease and may provide a new diagnostic approach. However, the data obtained from high-density microarrays is highly complex and poses considerable challenges in data mining. Past researches prove that neuro diseases damage the brain network interaction, protein- protein interaction and gene-gene interaction. A number of neurological research paper also analyze the relationship among damaged part. Analysis of gene-gene interaction network drawn by using state-of-the-art gene database of Alzheimer’s patient can conclude a lot of information. In this paper we used gene dataset affected with Alzheimer’s disease and normal patient’s dataset from NCBI databank. After proper processing the .CEL affymetrix data using RMA, we use the processed data to find gene interaction outputs. Then we filter the output files using probe set filtering attributes p-value and fold count and draw a gene-gene interaction network. Then we analyze the interaction network using GeneMania software.
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Genome-wide transcription profiling is a powerful technique in studying disease susceptible footprints. Moreover, when applied to disease tissue it may reveal quantitative and qualitative alterations in gene expression that give information on the context or underlying basis for the disease and may provide a new diagnostic approach. However, the data obtained from high-density microarrays is highly complex and poses considerable challenges in data mining. Past researches prove that neuro diseases damage the brain network interaction, protein- protein interaction and gene-gene interaction. A number of neurological research paper also analyze the relationship among damaged part. Analysis of gene-gene interaction network drawn by using state-of-the-art gene database of Alzheimer’s patient can conclude a lot of information. In this paper we used gene dataset affected with Alzheimer’s disease and normal patient’s dataset from NCBI databank. After proper processing the .CEL affymetrix data using RMA, we use the processed data to find gene interaction outputs. Then we filter the output files using probe set filtering attributes p-value and fold count and draw a gene-gene interaction network. Then we analyze the interaction network using GeneMania software.
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networks. This paper presents a detailed comparative
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A crucial task in modern biology is the prediction of complex phenotypes, such as breast cancer prognosis, from genome-wide measurements. Machine learning algorithms can sometimes infer predictive patterns, but there is rarely enough data to train and test them effectively and the patterns that they identify are often expressed in forms (e.g. support vector machines, neural networks, random forests composed of 10s of thousands of trees) that are highly difficult to understand. In addition, it is generally unclear how to include prior knowledge in the course of their construction.
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Branch enables expert biologists to easily engage directly with high-throughput datasets without the need for a team of bioinformaticians. The tree building process allows researchers to rapidly test hypotheses about interactions between biological variables and phenotypes in ways that would otherwise require extensive computational sophistication. In so doing, this tool can both inform biological research and help to produce more accurate, more meaningful classifiers.
A prototype of Branch is available at http://biobranch.org/
Summary on the Conference of WISE 2013
This is my summary on the conference of WISE 2013 (International Conference on Web Information System Engineering )
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ABSTRACT
Genome-wide transcription profiling is a powerful technique in studying disease susceptible footprints. Moreover, when applied to disease tissue it may reveal quantitative and qualitative alterations in gene expression that give information on the context or underlying basis for the disease and may provide a new diagnostic approach. However, the data obtained from high-density microarrays is highly complex and poses considerable challenges in data mining. Past researches prove that neuro diseases damage the brain network interaction, protein- protein interaction and gene-gene interaction. A number of neurological research paper also analyze the relationship among damaged part. Analysis of gene-gene interaction network drawn by using state-of-the-art gene database of Alzheimer’s patient can conclude a lot of information. In this paper we used gene dataset affected with Alzheimer’s disease and normal patient’s dataset from NCBI databank. After proper processing the .CEL affymetrix data using RMA, we use the processed data to find gene interaction outputs. Then we filter the output files using probe set filtering attributes p-value and fold count and draw a gene-gene interaction network. Then we analyze the interaction network using GeneMania software.
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Genome-wide transcription profiling is a powerful technique in studying disease susceptible footprints. Moreover, when applied to disease tissue it may reveal quantitative and qualitative alterations in gene expression that give information on the context or underlying basis for the disease and may provide a new diagnostic approach. However, the data obtained from high-density microarrays is highly complex and poses considerable challenges in data mining. Past researches prove that neuro diseases damage the brain network interaction, protein- protein interaction and gene-gene interaction. A number of neurological research paper also analyze the relationship among damaged part. Analysis of gene-gene interaction network drawn by using state-of-the-art gene database of Alzheimer’s patient can conclude a lot of information. In this paper we used gene dataset affected with Alzheimer’s disease and normal patient’s dataset from NCBI databank. After proper processing the .CEL affymetrix data using RMA, we use the processed data to find gene interaction outputs. Then we filter the output files using probe set filtering attributes p-value and fold count and draw a gene-gene interaction network. Then we analyze the interaction network using GeneMania software.
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This document summarizes Rebeen Ali Hamad's PhD thesis on developing robust deep learning models for human activity recognition using sensor data. The thesis addressed key challenges in HAR including imbalanced class problems and reducing the need for large labeled datasets. Some of the contributions included a dilated causal convolution model with self-attention to improve recognition accuracy, a joint temporal model to handle imbalanced data, and a cross-domain learning approach using shared representations to reduce labeling efforts. Evaluation results demonstrated improved performance over existing methods on several HAR datasets. Future work opportunities involve hybrid algorithm-data level models, better attention mechanisms, and recognizing multi-user concurrent activities.
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It has been decades since the evolution of idea that
human brain can be mimicked by artificial neuron like
mathematical structures. Till date, the development of this
endeavor has not reached the threshold of excellence. Neural
networks are commonly used to solve sample-recognition
problems. One of these is character recognition. The solution
of this problem is one of the easier implementations of neural
networks. This paper presents a detailed comparative
literature survey on the research accomplished for the last
few decades. The comparative literature review will help us
understand the platform on which we stand today to achieve
the highest efficiency in terms of Character Recognition
accuracy as well as computational resource and cost.
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This document provides a review of multispectral palm image fusion techniques. It begins with an introduction to biometrics and palm print identification. Different palm print images capture different spectral information about the palm. The document then reviews several pixel-level fusion methods for combining multispectral palm images, finding that Curvelet transform performs best at preserving discriminative patterns. It also discusses hardware for capturing multispectral palm images and the process of region of interest extraction and localization. Common fusion methods like wavelet transform and Curvelet transform are also summarized.
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forney_techrep2015
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Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Signal & Image processing.
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The document provides an overview of solutions from Quahog Life Sciences including data management, security, analysis, visualization, and applications. The platform allows merging of multiple data sources, creation of a logical data model, and organization of patient data. Advanced encryption is used to securely share data. The platform supports machine learning using a recursive neural network and analytics models. Use cases described include pattern discovery in cancer research and influencer detection in cellular research. Visualization capabilities include interactive dashboards with multiple chart types. Additional applications include bot assistance for diabetologists and physicians.
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the net input NN are one of the most exciting and challenging research areas. As ANN mature into commercial systems, they are likely
to be implemented in hardware. Their fault tolerance and reliability are therefore vital to the functioning of the system in which they
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ISSN : 0976 - 710X (Online) ; 2229 - 3922 (print)
http://www.airccse.org/journal/sipij/index.html
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presentation of PhD Thesis.pptx
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Research Statement Chien-Wei Lin
This document is a research statement by Chien-Wei (Masaki) Lin that summarizes his past and ongoing methodology and collaborative research projects. It discusses his interests in developing statistical methods for analyzing multi-omics data, including power calculation tools, meta-analysis and integrative analysis methods. It also summarizes some of Lin's collaboration projects applying these statistical methods to study topics like brain aging, major depressive disorder, and cardiovascular epidemiology. The document references 18 of Lin's publications and provides an overview of his diverse experience and future research plans developing statistical tools and methods and applying them to biological problems.
Evaluation of Logistic Regression and Neural Network Model With Sensitivity A...
Logistic Regression (LR) is a well known classification method in the field of statistical learning. It allows probabilistic classification and shows promising results on several benchmark problems. Logistic regression enables us to investigate the relationship between a categorical outcome and a set of explanatory variables. Artificial Neural Networks (ANNs) are popularly used as universal non-linear inference models and have gained extensive popularity in recent years. Research activities are considerable and literature is growing. The goal of this research work is to compare the performance of Logistic Regression and Neural Network models on publicly available medical datasets. The evaluation process of the model is as follows. The logistic regression and neural network methods with sensitivity analysis have been evaluated for the effectiveness of the classification. The Classification Accuracy is used to measure the performance of both the models. From the experimental results it is confirmed that the neural network model with sensitivity analysis model gives more efficient result.
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Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Signal & Image processing.
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The document provides an overview of solutions from Quahog Life Sciences including data management, security, analysis, visualization, and applications. The platform allows users to unify multiple data sources, merge them into a single structured data store, and organize data by patients. It uses advanced encryption techniques and offers machine learning capabilities like pattern extraction, segmentation, and predictive models. Visualization features include interactive dashboards with various chart types. Example use cases demonstrate pattern discovery in cancer research and influencer detection in cellular research. Bot applications are described for assisting diabetologists and physicians.
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The document provides an overview of solutions from Quahog Life Sciences including data management, security, analysis, visualization, and applications. The platform allows merging of multiple data sources, creation of a logical data model, and organization of patient data. Advanced encryption is used to securely share data. The platform supports machine learning using a recursive neural network and analytics models. Use cases described include pattern discovery in cancer research and influencer detection in cellular research. Visualization capabilities include interactive dashboards with multiple chart types. Additional applications include bot assistance for diabetologists and physicians.
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An artificial neural network (ANN) usually called neural network. It can be considered as a resemblance to a paradigm
which is inspired by biological nervous system. In network the signals are transmitted by the means of connections links. The links
possess an associated way which is multiplied along with the incoming signal. The output signal is obtained by applying activation to
the net input NN are one of the most exciting and challenging research areas. As ANN mature into commercial systems, they are likely
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- 1. Multi-View Design Patterns and Responsive Visualization for Genomics Data Seminar On Under the Guidance : Submitted By: Dr. S. B. Gurav Vidya Vijay Mali
- 2. Agenda Abstract Introduction Motivation Objective Literature survey Overview Diagram References 2
- 3. Abstract Genome data is very crucial in clinical trails Understanding the genome patterns are always a challenging task Using multi pattern analysis always enhances the model Using of Gosling grammar enhances the process of genome data Visualization 3
- 4. 4 Introduction Genome data Genome patterns Gosling Grammar Genome data visualization
- 5. Motivation Handling huge genome data is always a tedious work Identifying the pattern according to the need is a challenging task in genome data 5
- 6. Objectives To preprocess the data properly To enhance the model with Gosling grammar To identify the multi-pattern in genome tracks 6
- 7. LITERATURE SURVEY Paper Author Methodology Multi-View Design Patterns and Responsive Visualization for Genomics Data Sehi L’Yi and Nils Gehlenborg In this paper, Author’s provide a reusable and generalizable system for designing responsive genomics data multi-view visualizations in this research. Reviewing web-based genomics visualization tools in the wild helps us understand design difficulties. Using a taxonomy of responsive designs, Author’s discover that tools rarely promote responsiveness. Author’s identify typical view composition patterns such “vertically long,” “horizontally wide,” “circular,” and “cross- shaped” to organize survey findings. Then they identify their usability difficulties at multiple resolutions based on composition patterns and discuss ways to fix them and make genomics visualizations responsive. 7
- 8. LITERATURE SURVEY Paper Author Methodology Adaptively Exploring Population Mobility Patterns in Flow Visualization Fei Wang, Wei Chen, Ye Zhao, Tianyu Gu, Siyuan Gao, and Hujun Bao This paper presents a system that deciphers, transforms, searches, and visualizes records from millions of city users. Author’s created MobiHash, a data structure that receives phone call records from base stations and indexes them using a Voronoi partition of metropolitan space. MobiHash enables interactive retrieval of population flow trajectories in areas of interest using responsive data searches. To avoid visual clutter and occlusions, population movement is depicted as vector fields. A unique radiation model interpolates population passing zones due to sparse moving points. Author’s validated the usability and efficiency of our approach by analyzing population movement trends over time using case studies and expert feedback. 8
- 9. LITERATURE SURVEY Paper Author Methodology Active Brainwave Pattern Generation for Brain-To- Machine Communication Swathi Ganesh, Dale Timm, Kee S. Moon, Sung Q Lee, and Woosub Youm This paper aims to develop a real-time EEG-based brain-to-machine communication system by generating distinct signals and identifying their patterns for self-induced visual and auditory stimuli. The brain-to-machine communication system captures, analyzes, and visualizes brain signal patterns in real- time for medical applications like rehabilitation, robotic control, and smart wheelchairs. 9
- 11. References S. L'Yi and N. Gehlenborg, "Multi-View Design Patterns and Responsive Visualization for Genomics Data," in IEEE Transactions on Visualization and Computer Graphics, vol. 29, no. 1, pp. 559-569, Jan. 2023, doi: 10.1109/TVCG.2022.3209398. F. Wang, W. Chen, Y. Zhao, T. Gu, S. Gao and H. Bao, "Adaptively Exploring Population Mobility Patterns in Flow Visualization," in IEEE Transactions on Intelligent Transportation Systems, vol. 18, no. 8, pp. 2250-2259, Aug. 2017, doi: 10.1109/TITS.2017.2711644. S. Ganesh, D. Timm, K. S. Moon, S. Q. Lee and W. Youm, "Active brainwave pattern generation for brain-to-machine communication," 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Jeju, Korea (South), 2017, pp. 990-993, doi: 10.1109/EMBC.2017.8036992. 11