Literature DB >> 23202358

The Functional Genomics Network in the evolution of biological text mining over the past decade.

Christian Blaschke1, Alfonso Valencia.   

Abstract

Different programs of The European Science Foundation (ESF) have contributed significantly to connect researchers in Europe and beyond through several initiatives. This support was particularly relevant for the development of the areas related with extracting information from papers (text-mining) because it supported the field in its early phases long before it was recognized by the community. We review the historical development of text mining research and how it was introduced in bioinformatics. Specific applications in (functional) genomics are described like it's integration in genome annotation pipelines and the support to the analysis of high-throughput genomics experimental data, and we highlight the activities of evaluation of methods and benchmarking for which the ESF programme support was instrumental.
Copyright © 2013 Elsevier B.V. All rights reserved.

Mesh:

Year:  2012        PMID: 23202358     DOI: 10.1016/j.nbt.2012.11.020

Source DB:  PubMed          Journal:  N Biotechnol        ISSN: 1871-6784            Impact factor:   5.079


  5 in total

1.  RLIMS-P 2.0: A Generalizable Rule-Based Information Extraction System for Literature Mining of Protein Phosphorylation Information.

Authors:  Manabu Torii; Cecilia N Arighi; Gang Li; Qinghua Wang; Cathy H Wu; K Vijay-Shanker
Journal:  IEEE/ACM Trans Comput Biol Bioinform       Date:  2015 Jan-Feb       Impact factor: 3.710

Review 2.  Event-based text mining for biology and functional genomics.

Authors:  Sophia Ananiadou; Paul Thompson; Raheel Nawaz; John McNaught; Douglas B Kell
Journal:  Brief Funct Genomics       Date:  2014-06-06       Impact factor: 4.241

3.  Extracting rate changes in transcriptional regulation from MEDLINE abstracts.

Authors:  Wenting Liu; Kui Miao; Guangxia Li; Kuiyu Chang; Jie Zheng; Jagath C Rajapakse
Journal:  BMC Bioinformatics       Date:  2014-01-24       Impact factor: 3.169

4.  Multiple kernels learning-based biological entity relationship extraction method.

Authors:  Xu Dongliang; Pan Jingchang; Wang Bailing
Journal:  J Biomed Semantics       Date:  2017-09-20

5.  Literature-based automated discovery of tumor suppressor p53 phosphorylation and inhibition by NEK2.

Authors:  Byung-Kwon Choi; Tajhal Dayaram; Neha Parikh; Angela D Wilkins; Meena Nagarajan; Ilya B Novikov; Benjamin J Bachman; Sung Yun Jung; Peter J Haas; Jacques L Labrie; Curtis R Pickering; Anbu K Adikesavan; Sam Regenbogen; Linda Kato; Ana Lelescu; Christie M Buchovecky; Houyin Zhang; Sheng Hua Bao; Stephen Boyer; Griff Weber; Kenneth L Scott; Ying Chen; Scott Spangler; Lawrence A Donehower; Olivier Lichtarge
Journal:  Proc Natl Acad Sci U S A       Date:  2018-09-28       Impact factor: 11.205

  5 in total

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