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Statistical Modeling in Biomedical Research

Contemporary Topics and Voices in the Field

  • Book
  • © 2020

Overview

Editors:
  1. Yichuan Zhao

    1. Math and Statistics, 1342, Georgia State University, Atlanta, USA

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    You can also search for this editor in PubMed   Google Scholar

  2. Ding-Geng (Din) Chen

    1. School of Social Work, University of North Carolina, Chapel Hill, USA

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    You can also search for this editor in PubMed   Google Scholar

  • Includes a foundational overview of modeling in biomedical research to guide the reader in learning efficiently
  • Covers machine learning, GWAS data analysis, sequence analysis, and survival analysis in the big data era
  • Includes innovative statistical methods and applications in biomedical research

Part of the book series: Emerging Topics in Statistics and Biostatistics (ETSB)

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Table of contents (19 chapters)

  1. Front Matter

    Pages i-xviii
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  2. Next Generation Sequence Data Analysis

    1. Front Matter

      Pages 1-1
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    2. Modeling Species Specific Gene Expression Across Multiple Regions in the Brain

      • Liyang Diao, Ying Zhu, Nenad Sestan, Hongyu Zhao
      Pages 3-22

    3. Classification of EEG Motion Artifact Signals Using Spatial ICA

      • Hsin-Hsiung Huang, Aubrey Condor, Helen J. Huang
      Pages 23-35

    4. Weighted K-Means Clustering with Observation Weight for Single-Cell Epigenomic Data

      • Wenyu Zhang, Jiaxuan Wangwu, Zhixiang Lin
      Pages 37-64

    5. Discrete Multiple Testing in Detecting Differential Methylation Using Sequencing Data

      • Guanshengrui Hao, Nan Lin
      Pages 65-91

  3. Deep Learning, Precision Medicine and Applications

    1. Front Matter

      Pages 93-93
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    2. Prediction of Functional Markers of Mass Cytometry Data via Deep Learning

      • Claudia Solís-Lemus, Xin Ma, Maxwell Hostetter II, Suprateek Kundu, Peng Qiu, Daniel Pimentel-Alarcón
      Pages 95-104

    3. Building Health Application Recommender System Using Partially Penalized Regression

      • Eun Jeong Oh, Min Qian, Ken Cheung, David C. Mohr
      Pages 105-123

    4. Hierarchical Continuous Time Hidden Markov Model, with Application in Zero-Inflated Accelerometer Data

      • Zekun Xu, Eric B. Laber, Ana-Maria Staicu
      Pages 125-142

  4. Large Scale Data Analysis and Its Applications

    1. Front Matter

      Pages 143-143
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    2. Privacy Preserving Feature Selection via Voted Wrapper Method for Horizontally Distributed Medical Data

      • Yunmei Lu, Yanqing Zhang
      Pages 145-171

    3. Improving Maize Trait through Modifying Combination of Genes

      • Duolin Wang, Juexin Wang, Yu Chen, Sean Yang, Qin Zeng, Jingdong Liu et al.
      Pages 173-196

    4. Molecular Basis of Food Classification in Traditional Chinese Medicine

      • Xiaosong Han, Haiyan Zhao, Hao Xu, Yun Yang, Yanchun Liang, Dong Xu
      Pages 197-212

    5. Discovery Among Binary Biomarkers in Heterogeneous Populations

      • Junxian Geng, Elizabeth H. Slate
      Pages 213-232

  5. Biomedical Research and the Modelling

    1. Front Matter

      Pages 233-233
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    2. Heat Kernel Smoothing on Manifolds and Its Application to Hyoid Bone Growth Modeling

      • Moo K. Chung, Nagesh Adluru, Houri K. Vorperian
      Pages 235-261

    3. Optimal Projections in the Distance-Based Statistical Methods

      • Chuanping Yu, Xiaoming Huo
      Pages 263-308

    4. Kernel Tests for One, Two, and K-Sample Goodness-of-Fit: State of the Art and Implementation Considerations

      • Yang Chen, Marianthi Markatou
      Pages 309-337

    5. Hierarchical Modeling of the Effect of Pre-exposure Prophylaxis on HIV in the US

      • Renee Dale, Yingqing Chen, Hongyu He
      Pages 339-354
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Keywords

About this book

This edited collection discusses the emerging topics in statistical modeling for biomedical research. Leading experts in the frontiers of biostatistics and biomedical research discuss the statistical procedures, useful methods, and their novel applications in biostatistics research. Interdisciplinary in scope, the volume as a whole reflects the latest advances in statistical modeling in biomedical research, identifies impactful new directions, and seeks to drive the field forward. It also fosters the interaction of scholars in the arena, offering great opportunities to stimulate further collaborations. This book will appeal to industry data scientists and statisticians, researchers, and graduate students in biostatistics and biomedical science. It covers topics in:

  • Next generation sequence data analysis
  • Deep learning, precision medicine, and their applications
  • Large scale data analysis and its applications
  • Biomedical research and modeling
  • Survival analysis with complex data structure and its applications.

Editors and Affiliations

  • Math and Statistics, 1342, Georgia State University, Atlanta, USA

    Yichuan Zhao

  • School of Social Work, University of North Carolina, Chapel Hill, USA

    Ding-Geng (Din) Chen

About the editors

Professor Yichuan Zhao is a professor of statistics at Georgia State University. He has a joint appointment as associate member of the Neuroscience Institute, and he is also an affiliated faculty member at the School of Public Health at Georgia State University.  His current research interests focus on survival analysis, empirical likelihood method, nonparametric statistics, statistical analysis of ROC curves, bioinformatics, Monte Carlo methods, high-dimensional data analysis, and statistical modeling of fuzzy systems. He has published over 90 research articles in statistics and has co-edited three books on statistics, biostatistics & data science. In addition, he has been invited to deliver more than 180 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 a chair of the organizing committee to great success. He is currently serving as associate editor, or on the editorial board, for several statistical journals. Dr. Zhao is an elected member of the International Statistical Institute. 

Professor (Din) Ding-Geng Chen is a fellow of the American Statistical Association and currently the Wallace H. Kuralt Distinguished Professor at the University of North Carolina at Chapel Hill, and an extra-ordinary professor at the University of Pretoria. He was a professor at the University of Rochester and the Karl E. Peace Endowed Eminent Scholar Chair in biostatistics at Georgia Southern University. He is also a senior consultant for biopharmaceutical and government agencies, with extensive expertise in clinical trial biostatistics and public health statistics. Professor Chen has written more than 200 refereed publications and has co-authored/co-edited 28 books on clinical trial methodology, meta-analysis, causal inference, and public health statistics. 

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