This concise book for scientists and students interested in bioinformatics and data science covers all aspects of predictive modeling for biomarker discovery based on high-dimensional data, as well as modern data science methods for identification of parsimonious and robust multivariate biomarkers for medical diagnosis and personalized medicine.
This concise book for scientists and students interested in bioinformatics and data science covers all aspects of predictive modeling for biomarker discovery based on high-dimensional data, as well as modern data science methods for identification of parsimonious and robust multivariate biomarkers for medical diagnosis and personalized medicine.
Darius M. Dziuda, Ph.D., is Professor of Data Science and Bioinformatics at Central Connecticut State University (CCSU), with both academic and biotechnology industry experience. His research focuses on multivariate biomarker discovery for medical diagnosis, prognosis, and personalized medicine. Dr. Dziuda is also designing and teaching courses for two specializations of CCSU's graduate data science program: Bioinformatics and Advanced Data Science Methods.
Inhaltsangabe
Preface Acknowledgments Part I. Framework for Multivariate Biomarker Discovery: 1. Introduction 2. Multivariate analytics based on high-dimensional data: concepts and misconceptions 3. Predictive modeling for biomarker discovery 4. Evaluation of predictive models 5. Multivariate feature selection Part II. Regression Methods for Estimation: 6. Basic regression methods 7. Regularized regression methods 8. Regression with random forests 9. Support vector regression Part III. Classification Methods: 10. Classification with random forests 11. Classification with support vector machines 12. Discriminant analysis 13. Neural networks and deep learning Part IV. Biomarker Discovery via Multistage Signal Enhancement and Identification of Essential Patterns: 14. Multistage signal enhancement 15. Essential patterns, essential variables, and interpretable biomarkers Part V. Multivariate Biomarker Discovery Studies: 16. Biomarker discovery study 1: searching for essential gene expression patterns and multivariate biomarkers that are common for multiple types of cancer 17. Biomarker discovery study 2: multivariate biomarkers for liver cancer References Index.
Preface Acknowledgments Part I. Framework for Multivariate Biomarker Discovery: 1. Introduction 2. Multivariate analytics based on high-dimensional data: concepts and misconceptions 3. Predictive modeling for biomarker discovery 4. Evaluation of predictive models 5. Multivariate feature selection Part II. Regression Methods for Estimation: 6. Basic regression methods 7. Regularized regression methods 8. Regression with random forests 9. Support vector regression Part III. Classification Methods: 10. Classification with random forests 11. Classification with support vector machines 12. Discriminant analysis 13. Neural networks and deep learning Part IV. Biomarker Discovery via Multistage Signal Enhancement and Identification of Essential Patterns: 14. Multistage signal enhancement 15. Essential patterns, essential variables, and interpretable biomarkers Part V. Multivariate Biomarker Discovery Studies: 16. Biomarker discovery study 1: searching for essential gene expression patterns and multivariate biomarkers that are common for multiple types of cancer 17. Biomarker discovery study 2: multivariate biomarkers for liver cancer References Index.
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