Integration and Visualization of Gene Selection and Gene Regulatory Networks for Cancer Genome helps readers identify and select the specific genes causing oncogenes. The book also addresses the validation of the selected genes using various classification techniques and performance metrics, making it a valuable source for cancer researchers, bioinformaticians, and researchers from diverse fields interested in applying systems biology approaches to their studies.
Integration and Visualization of Gene Selection and Gene Regulatory Networks for Cancer Genome helps readers identify and select the specific genes causing oncogenes. The book also addresses the validation of the selected genes using various classification techniques and performance metrics, making it a valuable source for cancer researchers, bioinformaticians, and researchers from diverse fields interested in applying systems biology approaches to their studies.
Dr. Mishra is currently working as an Associate Professor in the Department of Computer Science & Engineering, Vignana Bharathi Institute of Technology, Hyderabad and as former Head of the Department for the same institution. She was earlier working as an Assistant Professor, Department of Computer Science & Engineering, Institute of Technical Education & Research, Siksha O Anusandhan (Deemed-to-be University), Bhubaneswar, Odisha, India. She has guided 5 M.Tech thesis and more than 30 B.Tech students. Dr. Mishra has around 22 publications in various peer-reviewed journals and conference, 3 book chapters and 1 book to her credit. Her area of research is basically in Data Mining, Bioinformatics and Machine Learning. She is currently into the field of Geoinformatics and Deep Learning.
Inhaltsangabe
1. Literature Review2. SVM-BT-RFE: An Improved Gene Selection Framework Using Bayesian T-Test Embedded in Support Vector Machine (Recursive Feature Elimination) Algorithm3. Enhanced Gene Ranking Approaches Using Modified Trace Ratio Algorithm for Gene Expression Data4. SNR-TR Gene Ranking Method: A Signal-to-Noise Ratio Based Gene Selection Algorithm Using Trace Ratio for Gene Expression Data5. Visualization of Interactive Gene Regulatory Network Using Gene Selection Techniques from Expression Data6. Conclusion and Future Work
1. Literature Review2. SVM-BT-RFE: An Improved Gene Selection Framework Using Bayesian T-Test Embedded in Support Vector Machine (Recursive Feature Elimination) Algorithm3. Enhanced Gene Ranking Approaches Using Modified Trace Ratio Algorithm for Gene Expression Data4. SNR-TR Gene Ranking Method: A Signal-to-Noise Ratio Based Gene Selection Algorithm Using Trace Ratio for Gene Expression Data5. Visualization of Interactive Gene Regulatory Network Using Gene Selection Techniques from Expression Data6. Conclusion and Future Work
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