Big data analytics in biostatistics and bioinformatics Yichuan Zhao, Ding-Geng Chen, editors

Format:

Book

Author/Creator:

Zhao, Yichuan

Contributor:

Zhao, Yichuan, editor.

Chen, Ding-Geng, editor.

Series:

ICSA book series in statistics 2199-0999

ICSA Book Series in Statistics 2199-0999

Language:

English

Subjects (All):

Biometry.

Big data.

biometrics.

Physical Description:

1 online resource

Place of Publication:

Cham Springer [2026]

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

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 Hybrid Distance for Classification of Complex Biological Data Based on Elastic Shape Analysis of Curves and Topological Data Analysis of Point Clouds

Chapter 14 Bifurcation Analysis of an Analog Hopfield Neural Network with Three Time Delays

Chapter 15 Advancing Information Integration through Empirical Likelihood: Selective Reviews and a New Idea

Part IV: Clinical Trials and Survival Analysis

Chapter 16 Hierarchical Semi-parametric Bayesian Modeling in Patient Screening and Enrollment Dynamic Prediction for Multicenter Clinical Trials

Chapter 17 Comparative Effectiveness Analysis of Lobectomy and Limited Resection for Elderly Non-Small Cell Lung Cancer Patients via Emulation

Chapter 18 Recent Developments in Joint Modeling for Recurrent Gap Times with a Terminal Event

Chapter 19 A Conditional Modelling Approach for Dynamic Risk Prediction of a Survival Outcome Using Longitudinal Biomarkers with an Application to Ovarian Cancer

Notes:

Includes index

Online resource; title from PDF title page (SpringerLink, viewed May 4, 2026)

ISBN:

9783032066497

3032066492

OCLC:

1588479892

Access Restriction:

Restricted for use by site license