Literature DB >> 16418747

Literature mining for the biologist: from information retrieval to biological discovery.

Lars Juhl Jensen1, Jasmin Saric, Peer Bork.   

Abstract

For the average biologist, hands-on literature mining currently means a keyword search in PubMed. However, methods for extracting biomedical facts from the scientific literature have improved considerably, and the associated tools will probably soon be used in many laboratories to automatically annotate and analyse the growing number of system-wide experimental data sets. Owing to the increasing body of text and the open-access policies of many journals, literature mining is also becoming useful for both hypothesis generation and biological discovery. However, the latter will require the integration of literature and high-throughput data, which should encourage close collaborations between biologists and computational linguists.

Mesh:

Year:  2006        PMID: 16418747     DOI: 10.1038/nrg1768

Source DB:  PubMed          Journal:  Nat Rev Genet        ISSN: 1471-0056            Impact factor:   53.242


  194 in total

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Journal:  Bioinformatics       Date:  2012-01-11       Impact factor: 6.937

2.  SEACOIN--an investigative tool for biomedical informatics researchers.

Authors:  Eva K Lee; Hee-Rin Lee; Alexander Quarshie
Journal:  AMIA Annu Symp Proc       Date:  2011-10-22

3.  Campus perspective on the National Institutes of Health public access policy: University of California, San Francisco, library experience.

Authors:  Marcus A Banks; Gail L Persily
Journal:  J Med Libr Assoc       Date:  2010-07

4.  KID--an algorithm for fast and efficient text mining used to automatically generate a database containing kinetic information of enzymes.

Authors:  Stephanie Heinen; Bernhard Thielen; Dietmar Schomburg
Journal:  BMC Bioinformatics       Date:  2010-07-13       Impact factor: 3.169

5.  Biomedical text summarization to support genetic database curation: using Semantic MEDLINE to create a secondary database of genetic information.

Authors:  T Elizabeth Workman; Marcelo Fiszman; John F Hurdle; Thomas C Rindflesch
Journal:  J Med Libr Assoc       Date:  2010-10

6.  A literature search tool for intelligent extraction of disease-associated genes.

Authors:  Jae-Yoon Jung; Todd F DeLuca; Tristan H Nelson; Dennis P Wall
Journal:  J Am Med Inform Assoc       Date:  2013-09-02       Impact factor: 4.497

Review 7.  Network inference and network response identification: moving genome-scale data to the next level of biological discovery.

Authors:  Diogo F T Veiga; Bhaskar Dutta; Gábor Balázsi
Journal:  Mol Biosyst       Date:  2009-12-11

Review 8.  Computational approaches to phenotyping: high-throughput phenomics.

Authors:  Yves A Lussier; Yang Liu
Journal:  Proc Am Thorac Soc       Date:  2007-01

9.  Beegle: from literature mining to disease-gene discovery.

Authors:  Sarah ElShal; Léon-Charles Tranchevent; Alejandro Sifrim; Amin Ardeshirdavani; Jesse Davis; Yves Moreau
Journal:  Nucleic Acids Res       Date:  2015-09-17       Impact factor: 16.971

10.  Plasma Proteome Signature of Sepsis: a Functionally Connected Protein Network.

Authors:  Genaro Pimienta; Douglas M Heithoff; Alexandre Rosa-Campos; Minerva Tran; Jeffrey D Esko; Michael J Mahan; Jamey D Marth; Jeffrey W Smith
Journal:  Proteomics       Date:  2019-02-20       Impact factor: 3.984

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