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

[1]  Peter V. Henstock,et al.  Cellular imaging predictions of clinical drug-induced liver injury. , 2008, Toxicological sciences : an official journal of the Society of Toxicology.

[2]  Catherine Mahony,et al.  SEURAT: Safety Evaluation Ultimately Replacing Animal Testing—Recommendations for future research in the field of predictive toxicology , 2014, Archives of Toxicology.

[3]  Weida Tong,et al.  A testing strategy to predict risk for drug-induced liver injury in humans using high-content screen assays and the ‘rule-of-two’ model , 2014, Archives of Toxicology.

[4]  Trevor Hastie,et al.  Regularization Paths for Generalized Linear Models via Coordinate Descent. , 2010, Journal of statistical software.

[5]  Patrick Y. Muller,et al.  The determination and interpretation of the therapeutic index in drug development , 2012, Nature Reviews Drug Discovery.

[6]  Stephen W. Edwards,et al.  Integrating Publicly Available Data to Generate Computationally Predicted Adverse Outcome Pathways for Fatty Liver. , 2016, Toxicological sciences : an official journal of the Society of Toxicology.

[7]  Raghunandan M Kainkaryam,et al.  Grouping 34 Chemicals Based on Mode of Action Using Connectivity Mapping. , 2016, Toxicological sciences : an official journal of the Society of Toxicology.

[8]  Gordon M. Crippen,et al.  Data Mining the NCI60 to Predict Generalized Cytotoxicity , 2008, J. Chem. Inf. Model..

[9]  Samuel Kaski,et al.  Probabilistic retrieval and visualization of biologically relevant microarray experiments , 2009, BMC Bioinformatics.

[10]  Darrell R Abernethy,et al.  Systems pharmacology to predict drug toxicity: integration across levels of biological organization. , 2013, Annual review of pharmacology and toxicology.

[11]  Xavier Robin,et al.  pROC: an open-source package for R and S+ to analyze and compare ROC curves , 2011, BMC Bioinformatics.

[12]  Benjamin S. Baumer,et al.  Tidy data , 2022, Modern Data Science with R.

[13]  Paul A Clemons,et al.  The Connectivity Map: Using Gene-Expression Signatures to Connect Small Molecules, Genes, and Disease , 2006, Science.

[14]  R. Tagliaferri,et al.  Discovery of drug mode of action and drug repositioning from transcriptional responses , 2010, Proceedings of the National Academy of Sciences.

[15]  Manuel C. Peitsch,et al.  Systems Toxicology: From Basic Research to Risk Assessment , 2014, Chemical research in toxicology.

[16]  Ron Wehrens,et al.  The pls Package: Principal Component and Partial Least Squares Regression in R , 2007 .

[17]  Egon L. Willighagen,et al.  The Chemistry Development Kit (CDK) v2.0: atom typing, depiction, molecular formulas, and substructure searching , 2017, Journal of Cheminformatics.

[18]  M. Hamburg,et al.  Advancing Regulatory Science , 2011, Science.

[19]  Andreas Krämer,et al.  Causal analysis approaches in Ingenuity Pathway Analysis , 2013, Bioinform..

[20]  Krister Wennerberg,et al.  Identification of selective cytotoxic and synthetic lethal drug responses in triple negative breast cancer cells , 2016, Molecular Cancer.

[21]  Guy Perrière,et al.  MADE4: an R package for multivariate analysis of gene expression data , 2005, Bioinform..

[22]  Klaus Obermayer,et al.  A new summarization method for affymetrix probe level data , 2006, Bioinform..

[23]  D. Krewski,et al.  A Framework for the Next Generation of Risk Science , 2014, Environmental health perspectives.

[24]  Matthew E. Ritchie,et al.  limma powers differential expression analyses for RNA-sequencing and microarray studies , 2015, Nucleic acids research.

[25]  Mathieu Vinken,et al.  Adverse Outcome Pathways and Drug-Induced Liver Injury Testing. , 2015, Chemical research in toxicology.

[26]  Hiroshi Yamada,et al.  Open TG-GATEs: a large-scale toxicogenomics database , 2014, Nucleic Acids Res..

[27]  R. Shoemaker.  The NCI60 human tumour cell line anticancer drug screen , 2006, Nature Reviews Cancer.

[28]  K. Wanner,et al.  Methods and Principles in Medicinal Chemistry , 2007 .

[29]  Cassandra Willyard.  Foretelling toxicity: FDA researchers work to predict risk of liver injury from drugs , 2016, Nature Medicine.

[30]  Curtis Klaassen,et al.  Comprar Casarett & Doull's Toxicology: The Basic Science of Poisons | Curtis Klaassen | 9780071470513 | Mcgraw-Hill Education , 2008 .

[31]  R. Myers,et al.  Evolving gene/transcript definitions significantly alter the interpretation of GeneChip data , 2005, Nucleic acids research.

[32]  P Smith,et al.  Concordance of the toxicity of pharmaceuticals in humans and in animals. , 2000, Regulatory toxicology and pharmacology : RTP.

[33]  Jorrit J Hornberg,et al.  A high content screening assay to predict human drug-induced liver injury during drug discovery. , 2013, Journal of pharmacological and toxicological methods.

[34]  Zoubin Ghahramani,et al.  Probabilistic machine learning and artificial intelligence , 2015, Nature.

[35]  Ola Spjuth,et al.  Toward the Replacement of Animal Experiments through the Bioinformatics-driven Analysis of ‘Omics’ Data from Human Cell Cultures , 2015, Alternatives to laboratory animals : ATLA.

[36]  Elisabet Berggren,et al.  Evaluation of read-across argumentation according to the ECHA Read-Across Assessment Framework (RAAF) , 2016 .

[37]  Arie Regev,et al.  Drug-Induced Liver Injury and Drug Development: Industry Perspective , 2014, Seminars in Liver Disease.

[38]  S. Cummings,et al.  Designing Clinical Research: An Epidemiologic Approach , 1988 .

[39]  James H. Bullard,et al.  aroma.affymetrix: A generic framework in R for analyzing small to very large Affymetrix data sets in bounded memory , 2008 .

[40]  A. Hopkins.  Network pharmacology: the next paradigm in drug discovery. , 2008, Nature chemical biology.

[41]  Krister Wennerberg,et al.  Comprehensive data-driven analysis of the impact of chemoinformatic structure on the genome-wide biological response profiles of cancer cells to 1159 drugs , 2011, BMC Bioinformatics.

[42]  Frank Emmert-Streib,et al.  Gene set analysis approaches for RNA-seq data: performance evaluation and application guideline , 2015, Briefings Bioinform..

[43]  H Fang,et al.  The Liver Toxicity Knowledge Base: A Systems Approach to a Complex End Point , 2013, Clinical pharmacology and therapeutics.

[44]  Robert J Kavlock,et al.  Phenotypic screening of the ToxCast chemical library to classify toxic and therapeutic mechanisms , 2014, Nature Biotechnology.

[45]  Ali Shojaie,et al.  Inference in High Dimensions with the Penalized Score Test , 2014, 1401.2678.

[46]  Samuel Kaski,et al.  Probabilistic drug connectivity mapping , 2014, BMC Bioinformatics.

[47]  Min Zhang,et al.  A decade of toxicogenomic research and its contribution to toxicological science. , 2012, Toxicological sciences : an official journal of the Society of Toxicology.

[48]  Raphael Gottardo,et al.  Orchestrating high-throughput genomic analysis with Bioconductor , 2015, Nature Methods.

[49]  Yvonne Will,et al.  Use of micropatterned cocultures to detect compounds that cause drug-induced liver injury in humans. , 2013, Toxicological sciences : an official journal of the Society of Toxicology.

[50]  Hugh A Barton,et al.  Setting Clinical Exposure Levels of Concern for Drug-Induced Liver Injury (DILI) Using Mechanistic in vitro Assays. , 2015, Toxicological sciences : an official journal of the Society of Toxicology.

[51]  Jeffrey J. Walline,et al.  Designing Clinical Research: an Epidemiologic Approach, 2nd Ed. , 2001 .

[52]  Jonathan Taylor,et al.  Statistical learning and selective inference , 2015, Proceedings of the National Academy of Sciences.

[53]  Harold F. Gómez,et al.  Genomic Models of Short-Term Exposure Accurately Predict Long-Term Chemical Carcinogenicity and Identify Putative Mechanisms of Action , 2014, PloS one.

[54]  Thomas Lengauer,et al.  Improved scoring of functional groups from gene expression data by decorrelating GO graph structure , 2006, Bioinform..

[55]  Pablo Tamayo,et al.  Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[56]  Katarzyna H. Kaminska,et al.  Characterization of drug-induced transcriptional modules: towards drug repositioning and functional understanding , 2013, Molecular systems biology.

[57]  Rafael A Irizarry,et al.  Exploration, normalization, and summaries of high density oligonucleotide array probe level data. , 2003, Biostatistics.

[58]  Melvin E. Andersen,et al.  Developing tools for defining and establishing pathways of toxicity , 2015, Archives of Toxicology.

[59]  Matthew H Bridgland-Taylor,et al.  A correlation between the in vitro drug toxicity of drugs to cell lines that express human P450s and their propensity to cause liver injury in humans. , 2014, Toxicological sciences : an official journal of the Society of Toxicology.

[60]  S. H. Hussaini,et al.  Idiosyncratic drug-induced liver injury: an update on the 2007 overview , 2014, Expert opinion on drug safety.

[61]  R. Wehrens,et al.  Molecular Chemometrics , 2006 .

[62]  Hiroaki Kitano,et al.  Lessons from Toxicology: Developing a 21st-Century Paradigm for Medical Research , 2015, Environmental health perspectives.

[63]  M. Kieser,et al.  To test or not to test: Preliminary assessment of normality when comparing two independent samples , 2012, BMC Medical Research Methodology.

[64]  Ruili Huang,et al.  Modelling the Tox21 10 K chemical profiles for in vivo toxicity prediction and mechanism characterization , 2016, Nature Communications.

[65]  K. Wallace,et al.  Dose response considerations in risk assessment--an overview of recent ILSI activities. , 2008, Toxicology letters.

[66]  Ola Spjuth,et al.  Cancer biology, toxicology and alternative methods development go hand-in-hand. , 2014, Basic & clinical pharmacology & toxicology.

[67]  O. Kallioniemi,et al.  High-Throughput Cell-Based Screening of 4910 Known Drugs and Drug-like Small Molecules Identifies Disulfiram as an Inhibitor of Prostate Cancer Cell Growth , 2009, Clinical Cancer Research.

[68]  M. Aleo,et al.  Human drug‐induced liver injury severity is highly associated with dual inhibition of liver mitochondrial function and bile salt export pump , 2014, Hepatology.

[69]  Kris Sankaran,et al.  structSSI: Simultaneous and Selective Inference for Grouped or Hierarchically Structured Data. , 2014, Journal of statistical software.

[70]  M. Fielden,et al.  The liver pharmacological and xenobiotic gene response repertoire , 2008, Molecular systems biology.

[71]  T. Hartung.  Toxicology for the twenty-first century , 2009, Nature.

[72]  Melvin E. Andersen,et al.  Incorporating New Technologies Into Toxicity Testing and Risk Assessment: Moving From 21st Century Vision to a Data-Driven Framework , 2013, Toxicological sciences : an official journal of the Society of Toxicology.

[73]  Ron Wehrens,et al.  Chemometrics with R: Multivariate Data Analysis in the Natural Sciences and Life Sciences , 2011 .

[74]  F. Collins,et al.  Transforming Environmental Health Protection , 2008, Science.

[75]  Philip Hewitt,et al.  Key Challenges and Opportunities Associated with the Use of In Vitro Models to Detect Human DILI: Integrated Risk Assessment and Mitigation Plans , 2016, BioMed research international.

[76]  Nigel Greene,et al.  Developing structure-activity relationships for the prediction of hepatotoxicity. , 2010, Chemical research in toxicology.

[77]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[78]  A. Vannacci,et al.  Risk of acute and serious liver injury associated to nimesulide and other NSAIDs: data from drug‐induced liver injury case–control study in Italy , 2016, British journal of clinical pharmacology.

[79]  Di Wu,et al.  ROAST: rotation gene set tests for complex microarray experiments , 2010, Bioinform..