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Full Description
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. 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 Number 114613).
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.
