{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T00:44:27Z","timestamp":1775263467221,"version":"3.50.1"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1010367","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2022,8,23]],"date-time":"2022-08-23T00:00:00Z","timestamp":1661212800000}}],"reference-count":77,"publisher":"Public Library of Science (PLoS)","issue":"8","license":[{"start":{"date-parts":[[2022,8,11]],"date-time":"2022-08-11T00:00:00Z","timestamp":1660176000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000057","name":"National Institute of General Medical Sciences","doi-asserted-by":"publisher","award":["R01GM122845"],"award-info":[{"award-number":["R01GM122845"]}],"id":[{"id":"10.13039\/100000057","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000049","name":"National Institute on Aging","doi-asserted-by":"publisher","award":["R01AG057555"],"award-info":[{"award-number":["R01AG057555"]}],"id":[{"id":"10.13039\/100000049","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>Predictive modeling of drug-induced gene expressions is a powerful tool for phenotype-based compound screening and drug repurposing. State-of-the-art machine learning methods use a small number of fixed cell lines as a surrogate for predicting actual expressions in a new cell type or tissue, although it is well known that drug responses depend on a cellular context. Thus, the existing approach has limitations when applied to personalized medicine, especially for many understudied diseases whose molecular profiles are dramatically different from those characterized in the training data. Besides the gene expression, dose-dependent cell viability is another important phenotype readout and is more informative than conventional summary statistics (e.g., IC50) for characterizing clinical drug efficacy and toxicity. However, few computational methods can reliably predict the dose-dependent cell viability. To address the challenges mentioned above, we designed a new deep learning model, MultiDCP, to predict cellular context-dependent gene expressions and cell viability on a specific dosage. The novelties of MultiDCP include a knowledge-driven gene expression profile transformer that enables context-specific phenotypic response predictions of novel cells or tissues, integration of multiple diverse labeled and unlabeled omics data, the joint training of the multiple prediction tasks, and a teacher-student training procedure that allows us to utilize unreliable data effectively. Comprehensive benchmark studies suggest that MultiDCP outperforms state-of-the-art methods with unseen cell lines that are dissimilar from the cell lines in the supervised training in terms of gene expressions. The predicted drug-induced gene expressions demonstrate a stronger predictive power than noisy experimental data for downstream tasks. Thus, MultiDCP is a useful tool for transcriptomics-based drug repurposing and compound screening that currently rely on noisy high-throughput experimental data. We applied MultiDCP to repurpose individualized drugs for Alzheimer\u2019s disease in terms of efficacy and toxicity, suggesting that MultiDCP is a potentially powerful tool for personalized drug discovery.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1010367","type":"journal-article","created":{"date-parts":[[2022,8,11]],"date-time":"2022-08-11T17:48:41Z","timestamp":1660240121000},"page":"e1010367","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":29,"title":["Deep learning prediction of chemical-induced dose-dependent and context-specific multiplex phenotype responses and its application to personalized alzheimer\u2019s disease drug repurposing"],"prefix":"10.1371","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4807-8504","authenticated-orcid":true,"given":"You","family":"Wu","sequence":"first","affiliation":[]},{"given":"Qiao","family":"Liu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9692-1290","authenticated-orcid":true,"given":"Yue","family":"Qiu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9051-2111","authenticated-orcid":true,"given":"Lei","family":"Xie","sequence":"additional","affiliation":[]}],"member":"340","published-online":{"date-parts":[[2022,8,11]]},"reference":[{"key":"pcbi.1010367.ref001","article-title":"Phenotypic screens as a renewed approach for drug discovery. (1878\u20135832 (Electronic)).","author":"W Zheng"},{"key":"pcbi.1010367.ref002","article-title":"The resurgence of phenotypic screening in drug discovery and development. (1746-045X (Electronic)).","author":"BK Wagner"},{"issue":"8","key":"pcbi.1010367.ref003","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1038\/nrd.2017.111","article-title":"Opportunities and challenges in phenotypic drug discovery: an industry perspective.","volume":"16","author":"JG Moffat","year":"2017","journal-title":"Nat Rev Drug Discov"},{"issue":"4","key":"pcbi.1010367.ref004","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1038\/clpt.2012.236","article-title":"Phenotypic vs. target-based drug discovery for first-in-class medicines","volume":"93","author":"DC Swinney","year":"2013","journal-title":"Clin Pharmacol Ther"},{"key":"pcbi.1010367.ref005","first-page":"9","article-title":"Recent advances in phenotypic drug discovery","author":"DC Swinney","year":"2020","journal-title":"F1000Res"},{"issue":"7391","key":"pcbi.1010367.ref006","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1038\/nature11003","article-title":"The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity","volume":"483","author":"J Barretina","year":"2012","journal-title":"Nature"},{"key":"pcbi.1010367.ref007","doi-asserted-by":"crossref","first-page":"D955","DOI":"10.1093\/nar\/gks1111","article-title":"Genomics of Drug Sensitivity in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells","volume":"41","author":"W Yang","year":"2013","journal-title":"Nucleic Acids Res"},{"issue":"46","key":"pcbi.1010367.ref008","doi-asserted-by":"crossref","first-page":"6918","DOI":"10.2174\/1381612822666161026154430","article-title":"Current Trends in Drug Sensitivity Prediction","volume":"22","author":"I Cortes-Ciriano","year":"2016","journal-title":"Curr Pharm Des"},{"issue":"5","key":"pcbi.1010367.ref009","first-page":"820","article-title":"Computational models for predicting drug responses in cancer research","volume":"18","author":"F. Azuaje","year":"2017","journal-title":"Brief Bioinform"},{"issue":"21","key":"pcbi.1010367.ref010","doi-asserted-by":"crossref","first-page":"1858","DOI":"10.2174\/1568026620666200710101307","article-title":"Current Advances and Limitations of Deep Learning in Anticancer Drug Sensitivity Prediction","volume":"20","author":"X Tan","year":"2020","journal-title":"Curr Top Med Chem"},{"issue":"1","key":"pcbi.1010367.ref011","doi-asserted-by":"crossref","first-page":"1661","DOI":"10.1038\/s41467-021-21898-7","article-title":"Integrated cross-study datasets of genetic dependencies in cancer","volume":"12","author":"C Pacini","year":"2021","journal-title":"Nat Commun"},{"issue":"1","key":"pcbi.1010367.ref012","article-title":"Deep generative neural network for accurate drug response imputation.","volume":"12","author":"P Jia","year":"2021","journal-title":"Nat Commun [Internet]"},{"key":"pcbi.1010367.ref013","doi-asserted-by":"crossref","first-page":"233","DOI":"10.3389\/fgene.2019.00233","article-title":"Autoencoder Based Feature Selection Method for Classification of Anticancer Drug Response","volume":"10","author":"X Xu","year":"2019","journal-title":"Front Genet."},{"issue":"22","key":"pcbi.1010367.ref014","doi-asserted-by":"crossref","first-page":"3907","DOI":"10.1093\/bioinformatics\/bty452","article-title":"Predicting Cancer Drug Response using a Recommender System","volume":"34","author":"C Suphavilai","year":"2018","journal-title":"Bioinformatics"},{"key":"pcbi.1010367.ref015","article-title":"Quantifying Drug Combination Synergy along Potency and Efficacy Axes. (2405\u20134712 (Print)).","author":"CT Meyer"},{"issue":"1","key":"pcbi.1010367.ref016","doi-asserted-by":"crossref","first-page":"1628","DOI":"10.1038\/s41598-018-38231-w","article-title":"Functional random forest with applications in dose-response predictions","volume":"9","author":"R Rahman","year":"2019","journal-title":"Sci Rep"},{"issue":"8","key":"pcbi.1010367.ref017","doi-asserted-by":"crossref","first-page":"944","DOI":"10.1021\/acscentsci.8b00529","article-title":"Mining Gene Expression Data for Drug Discovery.","volume":"4","author":"M. Pandika","year":"2018","journal-title":"ACS Cent Sci"},{"key":"pcbi.1010367.ref018","article-title":"Network perturbation analysis of gene transcriptional profiles reveals protein targets and mechanism of action of drugs and influenza A viral infection. (1362\u20134962 (Electronic)).","author":"H Noh"},{"key":"pcbi.1010367.ref019","article-title":"Elucidating Compound Mechanism of Action by Network Perturbation Analysis. (1097\u20134172 (Electronic)).","author":"JH Woo"},{"key":"pcbi.1010367.ref020","article-title":"Drug-induced adverse events prediction with the LINCS L1000 data. (1367\u20134811 (Electronic))","author":"Z Wang"},{"key":"pcbi.1010367.ref021","article-title":"LINCS Canvas Browser: interactive web app to query, browse and interrogate LINCS L1000 gene expression signatures. (1362\u20134962 (Electronic)).","author":"Q Duan"},{"issue":"3","key":"pcbi.1010367.ref022","doi-asserted-by":"crossref","first-page":"813","DOI":"10.1093\/bioinformatics\/btz645","article-title":"DeepCOP: deep learning-based approach to predict gene regulating effects of small molecules","volume":"36","author":"G Woo","year":"2020","journal-title":"Bioinformatics"},{"key":"pcbi.1010367.ref023","article-title":"A deep learning framework for high-throughput mechanism-driven phenotype compound screening and its application to COVID-19 drug repurposing","author":"T-H Pham","year":"2021","journal-title":"Nature Machine Intelligence"},{"key":"pcbi.1010367.ref024","article-title":"CeDR Atlas: a knowledgebase of cellular drug response. (1362\u20134962 (Electronic))","author":"YY Wang"},{"key":"pcbi.1010367.ref025","article-title":"Deconvolution of cell type-specific drug responses in human tumor tissue with single-cell RNA-seq. (1756-994X (Electronic)).","author":"W Zhao"},{"key":"pcbi.1010367.ref026","doi-asserted-by":"crossref","DOI":"10.1145\/2939672.2939754","article-title":"node2vec: Scalable Feature Learning for Networks","author":"A Grover","year":"2016","journal-title":"Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining."},{"issue":"D1","key":"pcbi.1010367.ref027","doi-asserted-by":"crossref","first-page":"D607","DOI":"10.1093\/nar\/gky1131","article-title":"STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets","volume":"47","author":"D Szklarczyk","year":"2019","journal-title":"Nucleic Acids Res"},{"key":"pcbi.1010367.ref028","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1145\/1143844.1143874","volume-title":"Proceedings of the 23rd international conference on Machine learning","author":"J Davis","year":"2006"},{"key":"pcbi.1010367.ref029","article-title":"The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets. (1932\u20136203 (Electronic))","author":"T Saito"},{"key":"pcbi.1010367.ref030","first-page":"1","article-title":"ROC Graphs: Notes and Practical Considerations for Researchers","volume":"31","author":"T. Fawcett","year":"2004","journal-title":"Machine Learning"},{"issue":"10","key":"pcbi.1010367.ref031","doi-asserted-by":"crossref","first-page":"1113","DOI":"10.1038\/ng.2764","article-title":"The Cancer Genome Atlas Pan-Cancer analysis project","volume":"45","author":"Cancer Genome Atlas Research N","year":"2013","journal-title":"Nat Genet"},{"key":"pcbi.1010367.ref032","article-title":"Neural Machine Translation by Jointly Learning to Align and Translate","author":"D Bahdanau","year":"2015","journal-title":"CoRR"},{"key":"pcbi.1010367.ref033","article-title":"Effective Approaches to Attention-based Neural Machine Translation.","author":"T Luong","year":"2015","journal-title":"ArXiv"},{"key":"pcbi.1010367.ref034","article-title":"Attention is All you Need","author":"A Vaswani","year":"2017","journal-title":"ArXiv"},{"key":"pcbi.1010367.ref035","article-title":"TranSynergy: Mechanism-Driven Interpretable Deep Neural Network for the Synergistic Prediction and Pathway Deconvolution of Drug Combinations","author":"Q Liu","year":"2020","journal-title":"bioRxiv"},{"key":"pcbi.1010367.ref036","article-title":"A deep learning framework for elucidating whole-genome chemical interaction space","author":"T Cai","year":"2020","journal-title":"bioRxiv"},{"issue":"10","key":"pcbi.1010367.ref037","doi-asserted-by":"crossref","first-page":"e1007480","DOI":"10.1371\/journal.pcbi.1007480","article-title":"Prediction of off-target specificity and cell-specific fitness of CRISPR-Cas System using attention boosted deep learning and network-based gene feature","volume":"15","author":"Q Liu","year":"2019","journal-title":"PLoS Comput Biol"},{"key":"pcbi.1010367.ref038","doi-asserted-by":"crossref","first-page":"75","DOI":"10.3389\/fgene.2020.00075","article-title":"DSPLMF: A Method for Cancer Drug Sensitivity Prediction Using a Novel Regularization Approach in Logistic Matrix Factorization.","volume":"11","author":"A Emdadi","year":"2020","journal-title":"Front Genet"},{"issue":"7480","key":"pcbi.1010367.ref039","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1038\/nature12831","article-title":"Inconsistency in large pharmacogenomic studies","volume":"504","author":"B Haibe-Kains","year":"2013","journal-title":"Nature"},{"key":"pcbi.1010367.ref040","doi-asserted-by":"crossref","first-page":"2333","DOI":"10.12688\/f1000research.9611.1","article-title":"Revisiting inconsistency in large pharmacogenomic studies","volume":"5","author":"Z Safikhani","year":"2016","journal-title":"F1000Res"},{"issue":"9","key":"pcbi.1010367.ref041","doi-asserted-by":"crossref","first-page":"2787","DOI":"10.1093\/bioinformatics\/btaa064","article-title":"A Bayesian approach to accurate and robust signature detection on LINCS L1000 data","volume":"36","author":"Y Qiu","year":"2020","journal-title":"Bioinformatics"},{"issue":"4","key":"pcbi.1010367.ref042","first-page":"1998","article-title":"Model-Based Clustering With Data Correction For Removing Artifacts In Gene Expression Data.","volume":"11","author":"WC Young","year":"2016","journal-title":"Ann Appl Stat"},{"key":"pcbi.1010367.ref043","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1002\/9780470059210.ch13","article-title":"Medical Dictionary for Regulatory Activities (MedDRA).","author":"E. Brown","year":"2006","journal-title":"Pharmacovigilance"},{"issue":"D1","key":"pcbi.1010367.ref044","doi-asserted-by":"crossref","first-page":"D1075","DOI":"10.1093\/nar\/gkv1075","article-title":"The SIDER database of drugs and side effects","volume":"44","author":"M Kuhn","year":"2016","journal-title":"Nucleic Acids Res"},{"issue":"125","key":"pcbi.1010367.ref045","doi-asserted-by":"crossref","first-page":"125ra31","DOI":"10.1126\/scitranslmed.3003377","article-title":"Data-driven prediction of drug effects and interactions","volume":"4","author":"NP Tatonetti","year":"2012","journal-title":"Sci Transl Med"},{"issue":"D1","key":"pcbi.1010367.ref046","doi-asserted-by":"crossref","first-page":"D1074","DOI":"10.1093\/nar\/gkx1037","article-title":"DrugBank 5.0: a major update to the DrugBank database for 2018","volume":"46","author":"DS Wishart","year":"2018","journal-title":"Nucleic Acids Res"},{"issue":"43","key":"pcbi.1010367.ref047","doi-asserted-by":"crossref","first-page":"15545","DOI":"10.1073\/pnas.0506580102","article-title":"Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles","volume":"102","author":"A Subramanian","year":"2005","journal-title":"Proc Natl Acad Sci U S A"},{"issue":"1","key":"pcbi.1010367.ref048","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1124\/mol.105.015206","article-title":"Mefenamic acid shows neuroprotective effects and improves cognitive impairment in in vitro and in vivo Alzheimer\u2019s disease models","volume":"69","author":"Y Joo","year":"2006","journal-title":"Mol Pharmacol"},{"issue":"1","key":"pcbi.1010367.ref049","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.neulet.2005.11.066","article-title":"Acetylsalicylic acid decreases tau phosphorylation at serine 422","volume":"396","author":"E Tortosa","year":"2006","journal-title":"Neurosci Lett"},{"issue":"6","key":"pcbi.1010367.ref050","doi-asserted-by":"crossref","first-page":"649","DOI":"10.1111\/jnc.14345","article-title":"The GABAergic system as a therapeutic target for Alzheimer\u2019s disease","volume":"146","author":"B Calvo-Flores Guzman","year":"2018","journal-title":"J Neurochem"},{"key":"pcbi.1010367.ref051","doi-asserted-by":"crossref","first-page":"175","DOI":"10.3389\/fnagi.2019.00175","article-title":"Dopamine and Dopamine Receptors in Alzheimer\u2019s Disease: A Systematic Review and Network Meta-Analysis","volume":"11","author":"X Pan","year":"2019","journal-title":"Front Aging Neurosci"},{"issue":"2","key":"pcbi.1010367.ref052","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.brainresbull.2010.11.004","article-title":"Roles of beta-adrenergic receptors in Alzheimer\u2019s disease: implications for novel therapeutics","volume":"84","author":"JT Yu","year":"2011","journal-title":"Brain Res Bull"},{"issue":"3","key":"pcbi.1010367.ref053","doi-asserted-by":"crossref","first-page":"1032","DOI":"10.1124\/jpet.107.120311","article-title":"GSK189254, a novel H3 receptor antagonist that binds to histamine H3 receptors in Alzheimer\u2019s disease brain and improves cognitive performance in preclinical models","volume":"321","author":"AD Medhurst","year":"2007","journal-title":"J Pharmacol Exp Ther"},{"key":"pcbi.1010367.ref054","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.neuropharm.2015.05.007","article-title":"Neuronal histamine and cognitive symptoms in Alzheimer\u2019s disease","volume":"106","author":"A Zlomuzica","year":"2016","journal-title":"Neuropharmacology"},{"issue":"3","key":"pcbi.1010367.ref055","doi-asserted-by":"crossref","first-page":"458","DOI":"10.1016\/j.nurt.2008.05.008","article-title":"5-HT6 receptor antagonists as novel cognitive enhancing agents for Alzheimer\u2019s disease","volume":"5","author":"N Upton","year":"2008","journal-title":"Neurotherapeutics"},{"issue":"1","key":"pcbi.1010367.ref056","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/j.expneurol.2006.08.008","article-title":"Caffeine and adenosine A(2a) receptor antagonists prevent beta-amyloid (25\u201335)-induced cognitive deficits in mice","volume":"203","author":"OP Dall\u2019Igna","year":"2007","journal-title":"Exp Neurol"},{"issue":"6","key":"pcbi.1010367.ref057","doi-asserted-by":"crossref","first-page":"3375","DOI":"10.1167\/iovs.13-12823","article-title":"Sigma receptor ligand, (+)-pentazocine, suppresses inflammatory responses of retinal microglia","volume":"55","author":"J Zhao","year":"2014","journal-title":"Invest Ophthalmol Vis Sci"},{"issue":"1","key":"pcbi.1010367.ref058","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1111\/j.1447-0594.2012.00866.x","article-title":"Effects of cilostazol on cognition and regional cerebral blood flow in patients with Alzheimer\u2019s disease and cerebrovascular disease: a pilot study","volume":"13","author":"H Sakurai","year":"2013","journal-title":"Geriatr Gerontol Int"},{"key":"pcbi.1010367.ref059","article-title":"Neuroinflammation and microglial activation in Alzheimer disease: where do we go from here? (1759\u20134766 (Electronic)).","author":"F Leng"},{"key":"pcbi.1010367.ref060","article-title":"Repurposing ibudilast to mitigate Alzheimer\u2019s disease by targeting inflammation","author":"G Oliveros","journal-title":"LID\u2014awac136"},{"issue":"1","key":"pcbi.1010367.ref061","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1038\/nprot.2008.211","article-title":"Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources","volume":"4","author":"W Huang da","year":"2009","journal-title":"Nat Protoc"},{"issue":"3","key":"pcbi.1010367.ref062","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1016\/S0197-4580(02)00132-X","article-title":"Cyclin C expression is involved in the pathogenesis of Alzheimer\u2019s disease","volume":"24","author":"U Ueberham","year":"2003","journal-title":"Neurobiol Aging"},{"issue":"13","key":"pcbi.1010367.ref063","doi-asserted-by":"crossref","DOI":"10.3390\/ijms21134812","article-title":"Steroids and Alzheimer\u2019s Disease: Changes Associated with Pathology and Therapeutic Potential.","volume":"21","author":"Y Akwa","year":"2020","journal-title":"Int J Mol Sci"},{"issue":"6","key":"pcbi.1010367.ref064","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1038\/s41419-020-2647-1","article-title":"Aberrant activation of neuronal cell cycle caused by dysregulation of ubiquitin ligase Itch results in neurodegeneration","volume":"11","author":"M Chauhan","year":"2020","journal-title":"Cell Death Dis"},{"key":"pcbi.1010367.ref065","article-title":"Neuroactive steroids, WIN-compounds and cholesterol share a common binding site on muscarinic acetylcholine receptors. (1873\u20132968 (Electronic)).","author":"E Dolej\u0161\u00ed"},{"issue":"3","key":"pcbi.1010367.ref066","first-page":"506","article-title":"A review of connectivity map and computational approaches in pharmacogenomics","volume":"19","author":"A Musa","year":"2018","journal-title":"Brief Bioinform"},{"key":"pcbi.1010367.ref067","article-title":"Cell Viability Assays.","author":"TL Riss","journal-title":"BTI\u2014Assay Guidance Manual"},{"issue":"1","key":"pcbi.1010367.ref068","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1093\/bioinformatics\/btab580","article-title":"A cross-level information transmission network for hierarchical omics data integration and phenotype prediction from a new genotype","volume":"38","author":"D He","year":"2022","journal-title":"Bioinformatics"},{"issue":"316","key":"pcbi.1010367.ref069","first-page":"316ra193","article-title":"Tissue transcriptome-driven identification of epidermal growth factor as a chronic kidney disease biomarker","volume":"7","author":"W Ju","year":"2015","journal-title":"Sci Transl Med"},{"issue":"6","key":"pcbi.1010367.ref070","doi-asserted-by":"crossref","first-page":"882","DOI":"10.1093\/bioinformatics\/bts034","article-title":"The sva package for removing batch effects and other unwanted variation in high-throughput experiments","volume":"28","author":"JT Leek","year":"2012","journal-title":"Bioinformatics"},{"issue":"D1","key":"pcbi.1010367.ref071","doi-asserted-by":"crossref","first-page":"D994","DOI":"10.1093\/nar\/gkx911","article-title":"PharmacoDB: an integrative database for mining in vitro anticancer drug screening studies","volume":"46","author":"P Smirnov","year":"2018","journal-title":"Nucleic Acids Res"},{"key":"pcbi.1010367.ref072","author":"G. Landrum","year":"2016","journal-title":"RDKit: Open-Source Cheminformatics Software"},{"issue":"5814","key":"pcbi.1010367.ref073","doi-asserted-by":"crossref","first-page":"972","DOI":"10.1126\/science.1136800","article-title":"Clustering by passing messages between data points","volume":"315","author":"BJ Frey","year":"2007","journal-title":"Science"},{"issue":"s1","key":"pcbi.1010367.ref074","doi-asserted-by":"crossref","first-page":"S161","DOI":"10.3233\/JAD-179939","article-title":"Religious Orders Study and Rush Memory and Aging Project","volume":"64","author":"DA Bennett","year":"2018","journal-title":"J Alzheimers Dis"},{"key":"pcbi.1010367.ref075","doi-asserted-by":"crossref","first-page":"180185","DOI":"10.1038\/sdata.2018.185","article-title":"The Mount Sinai cohort of large-scale genomic, transcriptomic and proteomic data in Alzheimer\u2019s disease","volume":"5","author":"M Wang","year":"2018","journal-title":"Sci Data"},{"key":"pcbi.1010367.ref076","doi-asserted-by":"crossref","first-page":"160089","DOI":"10.1038\/sdata.2016.89","article-title":"Human whole genome genotype and transcriptome data for Alzheimer\u2019s and other neurodegenerative diseases","volume":"3","author":"M Allen","year":"2016","journal-title":"Sci Data"},{"issue":"5","key":"pcbi.1010367.ref077","doi-asserted-by":"crossref","first-page":"e1007869","DOI":"10.1371\/journal.pcbi.1007869","article-title":"PenDA, a rank-based method for personalized differential analysis: Application to lung cancer","volume":"16","author":"M Richard","year":"2020","journal-title":"PLoS Comput Biol"}],"updated-by":[{"DOI":"10.1371\/journal.pcbi.1010367","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2022,8,23]],"date-time":"2022-08-23T00:00:00Z","timestamp":1661212800000}}],"container-title":["PLOS Computational Biology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pcbi.1010367","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,14]],"date-time":"2023-02-14T10:52:34Z","timestamp":1676371954000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pcbi.1010367"}},"subtitle":[],"editor":[{"given":"James","family":"Gallo","sequence":"first","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2022,8,11]]},"references-count":77,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2022,8,11]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pcbi.1010367","relation":{"new_version":[{"id-type":"doi","id":"10.1371\/journal.pcbi.1010367","asserted-by":"object"}]},"ISSN":["1553-7358"],"issn-type":[{"value":"1553-7358","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,11]]}}}