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This book focuses on big data analytics in biostatistics and bioinformatics. As a contributed volume, it seeks to stimulate the growth of this dynamic field in the era of artificial intelligence. By presenting an overview of recent advances in biostatistics and bioinformatics through advanced statistical and machine learning methods, the book offers valuable insights for data scientists and biostatisticians working in public health, neuroscience, and related disciplines.
It is a useful reference for graduate students and researchers across academia, industry, and government.
List of contents
Part I: An Overview of Big Data Analytics in Biostatistics and Bioinformatics.- Chapter 1 Big Data Analytics in Biostatistics and Bioinformatics: The Past, The Present and The Future.- Chapter 2 Navigating Sample Size Dilemmas in ML-based Predictive Analytics: A Comprehensive Review.- Chapter 3 Moving Beyond Mean: Harnessing Big Data for Health Insights by Quantile Regression.- Chapter 4. Incorrect Model Selection Using R2 and Akaike Information Criterion in Big Data Analyses.- Chapter 5 False Discovery Control in Multiple Testing: A Brief Overview of Theories and Methodologies.- Part II: Statistical Methods of Bayesian Analysis and Gene Expression Data.- Chapter 6 Investigating and Assessing Diverse Strategies and Classification Techniques Applied in the Integration of Multi-Omics Data.- Chapter 7 Sparse Bayesian Clustering of Matrix Data.- Chapter 8 Bayesian Kernel Based Modeling and Selection of Genetic Pathways and Genes in Cancer Studies: A Step Toward Targeted Treatment Protocols.- Chapter 9 Using Guided Regularized Random Forests to Identify Important Biological Pathways and Genes.- Chapter 10 Ultrahigh-Dimensional Discriminant Analysis and Its Application to Gene Expression Data.- Part III: Deep Learning and Neural Network.- Chapter 11 Deep Image-on-scalar Regression Model with Hidden Confounders.- Chapter 12 Transfer Learning for Causal Effect Estimation.- Chapter 13
About the author
Yichuan Zhao is a Professor of Statistics at Georgia State University in Atlanta. He has a joint appointment as associate member of the Neuroscience Institute, and he is also an affiliated faculty member of the School of Public Health at Georgia State University. His current research interest focuses on survival analysis, empirical likelihood methods, nonparametric statistics, analysis of ROC curves, bioinformatics, Monte Carlo methods, and statistical modelling of fuzzy systems. He has published more than 100 research articles in statistics and biostatistics, has co-edited seven books on statistics, biostatistics and data science, and has been invited to deliver more than 200 research talks nationally and internationally. He is currently serving as associate editor, or on the editorial board, for several statistical journals including Electronic Journal of Statistics and Journal of Nonparametric Statistics. Dr. Zhao is a Fellow of the American Statistical Association, and an elected member of the International Statistical Institute. He serves on the Board of Directors, ICSA and the Chair-Elect, Risk Analysis Section, the American Statistical Association.
Ding-Geng Chen is an elected fellow of the American Statistical Association and is currently the executive director and professor in biostatistics at the College of Health Solutions, Arizona State University. Also, he is an elected member of the Academy of Science of South Africa (MASSAf) (https://www.assaf.org.za/), an extraordinary professor and the SARChI research chair in biostatistics at the University of Pretoria, and an honorary professor at the University of KwaZulu-Natal, South Africa. He is a senior biostatistics consultant for biopharmaceuticals and government agencies with extensive expertise in biostatistics, clinical trials, and public health statistics. Dr. Chen has more than 200 referred professional publications, co-authored 12 books and co-edited 29 books on clinical trial methodology, meta-analysis, data science, causal inference, and public health research.
Summary
This book focuses on big data analytics in biostatistics and bioinformatics. As a contributed volume, it seeks to stimulate the growth of this dynamic field in the era of artificial intelligence. By presenting an overview of recent advances in biostatistics and bioinformatics through advanced statistical and machine learning methods, the book offers valuable insights for data scientists and biostatisticians working in public health, neuroscience, and related disciplines.
It is a useful reference for graduate students and researchers across academia, industry, and government.
