Biomedical Literature Mining
, discusses the multiple facets of modern biomedical literature mining and its many applications in genomics and systems biology. The volume is divided into three sections focusing on information retrieval, integrated text-mining approaches and domain-specific mining methods. Written in the highly successful
Methods in Molecular Biology
series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols and key tips on troubleshooting and avoiding known pitfalls.
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.
Reihe
Auflage
Sprache
Verlagsort
Verlagsgruppe
Illustrationen
36 farbige Abbildungen, 15 s/w Abbildungen
XII, 288 p. 51 illus., 36 illus. in color.
ISBN-13
978-1-4939-0709-0 (9781493907090)
DOI
10.1007/978-1-4939-0709-0
Schweitzer Klassifikation
Introduction to Biomedical literature text mining: Context and Objectives.- Accessing Biomedical Literature in the Current Information Landscape.- Mapping of Biomedical Text to Concepts of Lexicons, Terminologies and Ontologies.- Drug Interaction Text Mining.- Biological Information Extraction and Co-occurence Analysis: State of the Art and Perspectives.- Roles of Text Mining in Protein Function Prediction.- Functional Molecular Units for Guiding Biomarker Panel Design.- Mining Biological Networks from Full-text Articles.- Scientific Collaboration Networks using Biomedical Text.- Predicting future discoveries from current scientific literature.- Mining Emerging Biomedical Literature for Understanding Disease Associations in Drug Discovery.- Integrating Literature and Data Mining to Rank Disease Candidate Genes.- Role of Text Mining in Early Identification of Potential Drug Safety Issues.- Systematic Drug Repositioning using Text Mining.- Mining the Electronic Health Record for Disease-Specific Knowledge.
