A transcriptomics data-driven gene space accurately predicts liver cytopathology and drug-induced liver injury

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Kohonen, Pekka
dc.contributor.author Parkkinen, Juuso A.
dc.contributor.author Willighagen, Egon L.
dc.contributor.author Ceder, Rebecca
dc.contributor.author Wennerberg, Krister
dc.contributor.author Kaski, Samuel
dc.contributor.author Grafström, Roland C.
dc.date.accessioned 2017-08-03T12:08:47Z
dc.date.available 2017-08-03T12:08:47Z
dc.date.issued 2017-07-03
dc.identifier.citation Kohonen , P , Parkkinen , J A , Willighagen , E L , Ceder , R , Wennerberg , K , Kaski , S & Grafström , R C 2017 , ' A transcriptomics data-driven gene space accurately predicts liver cytopathology and drug-induced liver injury ' NATURE COMMUNICATIONS , vol 8 , 15932 , pp. 1-15 . DOI: 10.1038/ncomms15932 en
dc.identifier.issn 2041-1723
dc.identifier.other PURE UUID: 54866a7b-82cb-4171-bfd8-ba0332c19207
dc.identifier.other PURE ITEMURL: https://research.aalto.fi/en/publications/a-transcriptomics-datadriven-gene-space-accurately-predicts-liver-cytopathology-and-druginduced-liver-injury(54866a7b-82cb-4171-bfd8-ba0332c19207).html
dc.identifier.other PURE LINK: http://www.scopus.com/inward/record.url?scp=85021759690&partnerID=8YFLogxK
dc.identifier.other PURE FILEURL: https://research.aalto.fi/files/14300780/ncomms15932.pdf
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/27377
dc.description.abstract Predicting unanticipated harmful effects of chemicals and drug molecules is a difficult and costly task. Here we utilize a 'big data compacting and data fusion' - concept to capture diverse adverse outcomes on cellular and organismal levels. The approach generates from transcriptomics data set a 'predictive toxicogenomics space' (PTGS) tool composed of 1,331 genes distributed over 14 overlapping cytotoxicity-related gene space components. Involving ∼2.5 × 108 data points and 1,300 compounds to construct and validate the PTGS, the tool serves to: explain dose-dependent cytotoxicity effects, provide a virtual cytotoxicity probability estimate intrinsic to omics data, predict chemically-induced pathological states in liver resulting from repeated dosing of rats, and furthermore, predict human drug-induced liver injury (DILI) from hepatocyte experiments. Analysing 68 DILI-annotated drugs, the PTGS tool outperforms and complements existing tests, leading to a hereto-unseen level of DILI prediction accuracy. en
dc.format.extent 1-15
dc.format.mimetype application/pdf
dc.language.iso en en
dc.relation.ispartofseries NATURE COMMUNICATIONS en
dc.relation.ispartofseries Volume 8 en
dc.rights openAccess en
dc.subject.other Chemistry(all) en
dc.subject.other Biochemistry, Genetics and Molecular Biology(all) en
dc.subject.other Physics and Astronomy(all) en
dc.subject.other 113 Computer and information sciences en
dc.title A transcriptomics data-driven gene space accurately predicts liver cytopathology and drug-induced liver injury en
dc.type A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä fi
dc.description.version Peer reviewed en
dc.contributor.department Karolinska Institutet
dc.contributor.department Aalto University
dc.contributor.department University of Helsinki
dc.contributor.department Department of Computer Science
dc.subject.keyword Chemistry(all)
dc.subject.keyword Biochemistry, Genetics and Molecular Biology(all)
dc.subject.keyword Physics and Astronomy(all)
dc.subject.keyword 113 Computer and information sciences
dc.identifier.urn URN:NBN:fi:aalto-201708036345
dc.identifier.doi 10.1038/ncomms15932
dc.type.version publishedVersion


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