Health Informatics Data Analysis

1. Front Matter

   Pages i-x

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2. Global Nonlinear Fitness Function for Protein Structures

   • Yun Xu, Changyu Hu, Yang Dai, Jie Liang

   Pages 1-35

3. Computational Methods for Mass Spectrometry Imaging: Challenges, Progress, and Opportunities

   • Chanchala D. Kaddi, May D. Wang

   Pages 37-49

4. Identification and Functional Annotation of LncRNAs in Human Disease

   • Qi Liao, Dechao Bu, Liang Sun, Haitao Luo, Yi Zhao

   Pages 51-60

5. Metabolomics Characterization of Human Diseases

   • Masahiro Sugimoto

   Pages 61-71

6. Metagenomics for Monitoring Environmental Biodiversity: Challenges, Progress, and Opportunities

   • Raghu Chandramohan, Cheng Yang, Yunpeng Cai, May D. Wang

   Pages 73-87

7. Clinical Assessment of Disease Risk Factors Using SNP Data and Bayesian Methods

   • Ivan Kozyryev, Jing Zhang

   Pages 89-102

8. Imaging Genetics: Information Fusion and Association Techniques Between Biomedical Images and Genetic Factors

   • Dongdong Lin, Vince D. Calhoun, Yu-Ping Wang

   Pages 103-114

9. Biomedical Imaging Informatics for Diagnostic Imaging Marker Selection

   • Sonal Kothari Phan, Ryan Hoffman, May D. Wang

   Pages 115-127

10. ECG Annotation and Diagnosis Classification Techniques

    • Yan Yan, Xingbin Qin, Lei Wang

    Pages 129-154

11. EEG Visualization and Analysis Techniques

    • Gregor Schreiber, Hong Lin, Jonathan Garza, Yuntian Zhang, Minghao Yang

    Pages 155-168

12. Big Health Data Mining

    • Chao Zhang, Shunfu Xu, Dong Xu

    Pages 169-184

13. Computational Infrastructure for Telehealth

    • Fedor Lehocki, Igor Kossaczky, Martin Homola, Marek Mydliar

    Pages 185-199

14. Healthcare Data Mining, Association Rule Mining, and Applications

    • Chih-Wen Cheng, May D. Wang

    Pages 201-210

This book provides a comprehensive overview of different biomedical data types, including both clinical and genomic data. Thorough explanations enable readers to explore key topics ranging from electrocardiograms to Big Data health mining and EEG analysis techniques. Each chapter offers a summary of the field and a sample analysis. Also covered are telehealth infrastructure, healthcare information association rules, methods for mass spectrometry imaging, environmental biodiversity, and the global nonlinear fitness function for protein structures. Diseases are addressed in chapters on functional annotation of lncRNAs in human disease, metabolomics characterization of human diseases, disease risk factors using SNP data and Bayesian methods, and imaging informatics for diagnostic imaging marker selection.


With the exploding accumulation of Electronic Health Records (EHRs), there is an urgent need for computer-aided analysis of heterogeneous biomedical datasets. Biomedical data is notorious for its diversified scales, dimensions, and volumes, and requires interdisciplinary technologies for visual illustration and digital characterization. Various computer programs and servers have been developed for these purposes by both theoreticians and engineers. 


This book is an essential reference for investigating the tools available for analyzing heterogeneous biomedical data. It is designed for professionals, researchers, and practitioners in biomedical engineering, diagnostics, medical electronics, and related industries.

• Health informatics
• Bioinformatics
• Electrocardiogram
• EEG
• Biomedical image
• Image fusion
• Electronic health record
• Genome
• Functional domain
• Post-translational modification
• Microarray
• High-thoughtput sequencing
• Information fusion
• Data mining
• Big data health mining
• Computational infrastructure
• Tele-health
• Metabolomics
• Mass spectrometry imaging
• OMIC

“It would be nice to enhance the examples presented throughout the book with a more detailed step-by-step guide for non-experts in the medical domain, including a reader-friendly analysis and interpretation of the results obtained along with a summary of lessons learned.” (Sergio Ilarri, Computing Reviews, April, 2018)​

• Digital Biology Laboratory, Computer Science Department, University of Missouri-Columbia, Columbia, USA

  Dong Xu

• Georgia Institute of Technology and Emory University, Atlanta, USA

  May D. Wang

• College of Computer Science and Technology, Jilin University, Changchun, China

  Fengfeng Zhou

• Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

  Yunpeng Cai

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