Big Data Analytics in Biostatistics and Bioinformatics

Yichuan Zhao, Ding-Geng Chen (Herausgeber)

Buch | Hardcover

X, 464 Seiten

2026
Springer International Publishing (Verlag)
978-3-032-06648-0 (ISBN)

Infos

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. 

Autor

Professor 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. Dr. Zhao has organized the Workshop Series on Biostatistics and Bioinformatics since its initiation in 2012. He also organized the 25th ICSA Applied Statistics Symposium in Atlanta as the chair of the organizing committee to great success. In addition, the 6th ICSA China Conference that he organized as the chair of both the organizing committee and program committee was a huge success.  He is currently serving as associate editor, or on the editorial board, for several statistical journals including Electronic Journal of Statistics, 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.

Professor 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. Dr. Chen was a professor in Biostatistics at the University of North Carolina at Chapel Hill, a professor in biostatistics at the University of Rochester Medical School, and the Karl E. Peace Endowed Eminent Scholar Chair in Biostatistics at Georgia Southern University. 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. This work is partially supported by the South African National Research Foundation (NRF) and South African Medical Research Council (SAMRC) (South African DST-NRF-SAMRC SARChI Research Chair in Biostatistics, Grant Numb

Inhalt

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

Produkt-Details

• Erscheinungsdatum: 29.11.2025
• Reihe/Serie: ICSA Book Series in Statistics
• Zusatzinfo: X, 464 p. 102 illus., 70 illus. in color.
• Verlagsort: Cham
• Sprache: englisch
• Maße: 155 x 235 mm
• Themenwelt: Mathematik / Informatik ► Informatik ► Datenbanken
• Themenwelt: Mathematik / Informatik ► Mathematik ► Wahrscheinlichkeit / Kombinatorik
• Schlagworte: adaptive design • Biological Pathways • causal effect estimation • classification • data integration • deep Image-on-scalar • False Discovery Rate • Feature screening • Graphical Models • hidden confounder • hierarchical model • Lasso • Limit of Detection • multiple testing • randomized trials • subset selection • transfer learning • Variable selection
• ISBN-10: 3-032-06648-4 / 3032066484
• ISBN-13: 978-3-032-06648-0 / 9783032066480
• Zustand: Neuware