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

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http://hdl.handle.net/10138/201577

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Kohonen , P , Parkkinen , J A , Willighagen , E L , Ceder , R , Wennerberg , K , Kaski , S & Grafstrom , R C 2017 , ' A transcriptomics data-driven gene space accurately predicts liver cytopathology and drug-induced liver injury ' , Nature Communications , vol. 8 , 15932 . https://doi.org/10.1038/ncomms15932

Title: A transcriptomics data-driven gene space accurately predicts liver cytopathology and drug-induced liver injury
Author: Kohonen, Pekka; Parkkinen, Juuso A.; Willighagen, Egon L.; Ceder, Rebecca; Wennerberg, Krister; Kaski, Samuel; Grafstrom, Roland C.
Contributor: University of Helsinki, Institute for Molecular Medicine Finland
University of Helsinki, Helsinki Institute for Information Technology
Date: 2017-07-03
Language: eng
Number of pages: 15
Belongs to series: Nature Communications
ISSN: 2041-1723
URI: http://hdl.handle.net/10138/201577
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 similar to 2.5 x 10(8) 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.
Subject: PROBE LEVEL DATA
RISK-ASSESSMENT
MICROARRAY EXPERIMENTS
EXPRESSION-DATA
SMALL MOLECULES
CANCER-CELLS
TOXICITY
TOXICOLOGY
HUMANS
DISCOVERY
3111 Biomedicine
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