Methods in Biomedical Informatics: A Pragmatic Approach

Beginning with a survey of fundamental concepts associated with data integration, knowledge representation, and hypothesis generation from heterogeneous data sets, Methods in Biomedical Informatics provides a practical survey of methodologies used in biological, clinical, and public health contexts. These concepts provide the foundation for more advanced topics like information retrieval, natural language processing, Bayesian modeling, and learning classifier systems. The survey of topics then concludes with an exposition of essential methods associated with engineering, personalized medicine, and linking of genomic and clinical data. Within an overall context of the scientific method, Methods in Biomedical Informatics provides a practical coverage of topics that is specifically designed for: (1) domain experts seeking an understanding of biomedical informatics approaches for addressing specific methodological needs; or (2) biomedical informaticians seeking an approachable overview of methodologies that can be used in scenarios germane to biomedical research.

Contributors represent leading biomedical informatics experts: individuals who have demonstrated effective use of biomedical informatics methodologies in the real-world, high-quality biomedical applicationsMaterial is presented as a balance between foundational coverage of core topics in biomedical informatics with practical "in-the-trenches" scenarios.Contains appendices that function as primers on: (1) Unix; (2) Ruby; (3) Databases; and (4) Web Services.

1. Introduction - Indra Neil Sarkar 2. Data Integration: An Overview - Prakash Nadkarni and Luis Marenco 3. Knowledge Representation - Mark A. Musen 4. Hypothesis Generation from Heterogenous Data Sets - Yves A. Lussier and Haiquan Li 5. Geometric Representations in Biomedical Informatics: Applications in Automated Text Analysis - Trevor Cohen and Dominic Widdows 6. Biomedical Natural Language Processing and Text Mining - Kevin B. Cohen 7. Knowledge Discovery in Biomedical Data: Theory and Methods - John H. Holmes 8. Bayesian Methods in Biomedical Data Analysis - Hsun-Hsien Chang and Gil Alterovitz 9. Learning Classifier Systems: The Rise of Genetics-Based Machine Learning in Biomedical Data Mining - Ryan J. Urbanowicz and Jason H. Moore 10. Engineering Principles in Biomedical Informatics - Riccardo Bellazzi, Matteo Gabetta, Giorgio Leonardi 11. Biomedical Informatics Methods for Personalized Medicine and Participatory Health - Fernando Martin-Sanchez, Guillermo Lopez-Campos, Kathleen Gray 12. Linking Genomic and Clinical Data for Discovery and Personalized Care - Joshua C. Denny and Hua Xu 13. Putting Theory into Practice - Indra Neil Sarkar

Appendices A1: Unix Primer - Elizabeth S. Chen A2: Ruby Primer - Elizabeth S. Chen A3: Database Primer - Elizabeth S. Chen A4: Web Services - Elizabeth S. Chen