Overview

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.

Full Product Details

Author:   Vinod D. Kumar ,  Hannah Jane Tipney
Publisher:   Humana Press Inc.
Imprint:   Humana Press Inc.
Edition:   2014 ed.
Volume:   1159
Dimensions:   Width: 17.80cm , Height: 2.30cm , Length: 25.40cm
Weight:   7.774kg
ISBN:  

9781493907083

ISBN 10:   1493907085
Pages:   288
Publication Date:   30 April 2014
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   Manufactured on demand  
We will order this item for you from a manufactured on demand supplier.

Table of Contents

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.

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