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Authoritative and practical, Biomedical Literature Mining is designed as a useful bioinformatics resource in biomedical literature text mining for both those long experienced in or entirely new to, the field.
1. Introduction to Biomedical Literature Text mining: Context and Objectives
Jeffrey D. Saffer and Vicki L. Burnett
Part I: Principles of Text mining: Getting Started
2. Accessing Biomedical Literature in the Current Information Landscape
Ritu Khare, Robert Leaman and Zhiyong Lu
3. Mapping of Biomedical Text to Concepts of Lexicons, Terminologies and Ontologies
Mike Bada
4. Drug Interaction Text Mining
Hengyi Wu, ChienWei Chiang, and Lang Li
5. Biological Information Extraction and Co-occurence Analysis: State of the Art and Perspectives
Georgios Pavlopoulos, Vasilis Promponas, Christos Ouzounis and Ioannis Iliopoulos
Part II: Seeking new Biology by Unlocking “Hidden” Information
6. Roles of Text Mining in Protein Function Prediction
Karin Verspoor
7. Functional Molecular Units for Guiding Biomarker Panel Design
Andreas Heinzel, Irmgard Mühlberger, Raul Fechete, Bernd Mayer, Paul Perco
8. Mining Biological Networks from Full-text Articles
Jan Czarnecki and Adrian J Shepherd
9. Scientific Collaboration Networks using Biomedical Text
Siddhartha Jonnalagadda, Philip Topham, Ryan Peeler, and Edward Silverman
10. Predicting Future Discoveries from Current Scientific Literature
Ingrid Petric, Tanja Urbancic, and Bojan Cestnik
Part III: From Electronic Biology to Drug Discovery and Development
11. Mining Emerging Biomedical Literature for Understanding Disease Associations in Drug Discovery
Deepak K. Rajpal, Xiaoyan A. Qu, Johannes Freudenberg, and Vinod Kumar
12. Integrating Literature and Data Mining to Rank Disease Candidate Genes
Chao Wu, Cheng Zhu, and Anil G Jegga
13. Role of Text Mining in Early Identification of Potential Drug Safety IssuesMei Liu, Yong Hu, and Buzhou Tang
14. Systematic Drug Repositioning using Text Mining
Luis Tari and Jagruti Patel
15. Mining the Electronic Health Record for Disease-Specific Knowledge
Elizabeth S. Chen and Indra Neil Sarkar
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