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Article: Discovery of perturbation gene targets via free text metadata mining in Gene Expression Omnibus

TitleDiscovery of perturbation gene targets via free text metadata mining in Gene Expression Omnibus
Authors
KeywordsFree text metadata
R/shiny
Gene perturbation
Machine learning
Issue Date2019
PublisherPergamon. The Journal's web site is located at http://www.elsevier.com/locate/cbac
Citation
Computational Biology and Chemistry, 2019, v. 80, p. 152-158 How to Cite?
AbstractThere exists over 2.5 million publicly available gene expression samples across 101,000 data series in NCBI's Gene Expression Omnibus (GEO) database. Due to the lack of the use of standardised ontology terms in GEO's free text metadata to annotate the experimental type and sample type, this database remains difficult to harness computationally without significant manual intervention. In this work, we present an interactive R/Shiny tool called GEOracle that utilises text mining and machine learning techniques to automatically identify perturbation experiments, group treatment and control samples and perform differential expression. We present applications of GEOracle to discover conserved signalling pathway target genes and identify an organ specific gene regulatory network. GEOracle is effective in discovering perturbation gene targets in GEO by harnessing its free text metadata. Its effectiveness and applicability has been demonstrated by cross validation and two real-life case studies. It opens up new avenues to unlock the gene regulatory information embedded inside large biological databases such as GEO. GEOracle is available at https://github.com/VCCRI/GEOracle.
Persistent Identifierhttp://hdl.handle.net/10722/271378
ISSN
2019 Impact Factor: 1.85
2015 SCImago Journal Rankings: 0.491

 

DC FieldValueLanguage
dc.contributor.authorDjordjevic, D-
dc.contributor.authorTang, JYS-
dc.contributor.authorChen, YX-
dc.contributor.authorKwan, SLS-
dc.contributor.authorLing, RWK-
dc.contributor.authorQian, G-
dc.contributor.authorWoo, CYY-
dc.contributor.authorEllis, SJ-
dc.contributor.authorHo, JWK-
dc.date.accessioned2019-06-24T01:08:43Z-
dc.date.available2019-06-24T01:08:43Z-
dc.date.issued2019-
dc.identifier.citationComputational Biology and Chemistry, 2019, v. 80, p. 152-158-
dc.identifier.issn1476-9271-
dc.identifier.urihttp://hdl.handle.net/10722/271378-
dc.description.abstractThere exists over 2.5 million publicly available gene expression samples across 101,000 data series in NCBI's Gene Expression Omnibus (GEO) database. Due to the lack of the use of standardised ontology terms in GEO's free text metadata to annotate the experimental type and sample type, this database remains difficult to harness computationally without significant manual intervention. In this work, we present an interactive R/Shiny tool called GEOracle that utilises text mining and machine learning techniques to automatically identify perturbation experiments, group treatment and control samples and perform differential expression. We present applications of GEOracle to discover conserved signalling pathway target genes and identify an organ specific gene regulatory network. GEOracle is effective in discovering perturbation gene targets in GEO by harnessing its free text metadata. Its effectiveness and applicability has been demonstrated by cross validation and two real-life case studies. It opens up new avenues to unlock the gene regulatory information embedded inside large biological databases such as GEO. GEOracle is available at https://github.com/VCCRI/GEOracle.-
dc.languageeng-
dc.publisherPergamon. The Journal's web site is located at http://www.elsevier.com/locate/cbac-
dc.relation.ispartofComputational Biology and Chemistry-
dc.subjectFree text metadata-
dc.subjectR/shiny-
dc.subjectGene perturbation-
dc.subjectMachine learning-
dc.titleDiscovery of perturbation gene targets via free text metadata mining in Gene Expression Omnibus-
dc.typeArticle-
dc.identifier.emailHo, JWK: jwkho@hku.hk-
dc.identifier.authorityHo, JWK=rp02436-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.compbiolchem.2019.03.014-
dc.identifier.pmid30959271-
dc.identifier.scopuseid_2-s2.0-85063864313-
dc.identifier.hkuros298181-
dc.identifier.volume80-
dc.identifier.spage152-
dc.identifier.epage158-
dc.publisher.placeUnited Kingdom-

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