{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,30]],"date-time":"2026-05-30T01:51:17Z","timestamp":1780105877664,"version":"3.54.0"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,1,7]],"date-time":"2021-01-07T00:00:00Z","timestamp":1609977600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2021,1,7]],"date-time":"2021-01-07T00:00:00Z","timestamp":1609977600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"This work is supported by National Natural Science Foundation of China","award":["81803431"],"award-info":[{"award-number":["81803431"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"published-print":{"date-parts":[[2021,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>Querying drug-induced gene expression profiles with machine learning method is an effective way for revealing drug mechanism of actions (MOAs), which is strongly supported by the growth of large scale and high-throughput gene expression databases. However, due to the lack of code-free and user friendly applications, it is not easy for biologists and pharmacologists to model MOAs with state-of-art deep learning approach.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>In this work, a newly developed online collaborative tool, Genetic profile-activity relationship (GPAR) was built to help modeling and predicting MOAs easily via deep learning. The users can use GPAR to customize their training sets to train self-defined MOA prediction models, to evaluate the model performances and to make further predictions automatically. Cross-validation tests show GPAR outperforms Gene set enrichment analysis in predicting MOAs.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>GPAR can serve as a better approach in MOAs prediction, which may facilitate researchers to generate more reliable MOA hypothesis.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12859-020-03915-6","type":"journal-article","created":{"date-parts":[[2021,1,7]],"date-time":"2021-01-07T12:03:47Z","timestamp":1610021027000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Modeling drug mechanism of action with large scale gene-expression profiles using GPAR, an artificial intelligence platform"],"prefix":"10.1186","volume":"22","author":[{"given":"Shengqiao","family":"Gao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lu","family":"Han","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dan","family":"Luo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiyong","family":"Xiao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guangcun","family":"Shan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongxiang","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenxia","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,1,7]]},"reference":[{"key":"3915_CR1","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1146\/annurev-biodatasci-072018-021211","volume":"2","author":"AB Keenan","year":"2019","unstructured":"Keenan AB, Wojciechowicz ML, Wang Z, Jagodnik KM, Jenkins SL, Lachmann A, et al. Connectivity mapping: methods and applications. Annu Rev Biomed Data Sci. 2019;2:69\u201392.","journal-title":"Annu Rev Biomed Data Sci"},{"key":"3915_CR2","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1038\/nrd.2018.168","volume":"18","author":"S Pushpakom","year":"2018","unstructured":"Pushpakom S, Iorio F, Eyers PA, Escott KJ, Hopper S, Wells A, et al. Drug repurposing: progress, challenges and recommendations. Nat Rev Drug Discov. 2018;18:41\u201358.","journal-title":"Nat Rev Drug Discov"},{"key":"3915_CR3","doi-asserted-by":"publisher","first-page":"15545","DOI":"10.1073\/pnas.0506580102","volume":"102","author":"A Subramanian","year":"2005","unstructured":"Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci. 2005;102:15545\u201350.","journal-title":"Proc Natl Acad Sci"},{"key":"3915_CR4","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1093\/bioinformatics\/bts656","volume":"29","author":"C Pacini","year":"2013","unstructured":"Pacini C, Iorio F, Gon\u00e7alves E, Iskar M, Klabunde T, Bork P, et al. DvD: an R\/Cytoscape pipeline for drug repurposing using public repositories of gene expression data. Bioinformatics. 2013;29:132\u20134.","journal-title":"Bioinformatics"},{"key":"3915_CR5","doi-asserted-by":"publisher","first-page":"1787","DOI":"10.1093\/bioinformatics\/btu058","volume":"30","author":"D Carrella","year":"2014","unstructured":"Carrella D, Napolitano F, Rispoli R, Miglietta M, Carissimo A, Cutillo L, et al. Mantra 2.0: an online collaborative resource for drug mode of action and repurposing by network analysis. Bioinformatics. 2014;30:1787\u20138.","journal-title":"Bioinformatics"},{"key":"3915_CR6","doi-asserted-by":"publisher","first-page":"3379","DOI":"10.1093\/bioinformatics\/btu560","volume":"30","author":"M Ni","year":"2014","unstructured":"Ni M, Ye F, Zhu J, Li Z, Yang S, Yang B, et al. ExpTreeDB: web-based query and visualization of manually annotated gene expression profiling experiments of human and mouse from GEO. Bioinformatics. 2014;30:3379\u201386.","journal-title":"Bioinformatics"},{"key":"3915_CR7","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1089\/omi.2012.0087","volume":"17","author":"F Li","year":"2013","unstructured":"Li F, Cao Y, Han L, Cui X, Xie D, Wang S, et al. GeneExpressionSignature: an R package for discovering functional connections using gene expression signatures. OMICS J Integr Biol. 2013;17:116\u20138.","journal-title":"OMICS J Integr Biol"},{"key":"3915_CR8","doi-asserted-by":"publisher","first-page":"2150","DOI":"10.1093\/bioinformatics\/bty060","volume":"34","author":"Z Wang","year":"2018","unstructured":"Wang Z, Lachmann A, Keenan AB, Ma\u2019ayan A. L1000FWD: fireworks visualization of drug-induced transcriptomic signatures. Bioinformatics. 2018;34:2150\u20132.","journal-title":"Bioinformatics"},{"key":"3915_CR9","doi-asserted-by":"publisher","DOI":"10.1038\/npjsba.2016.15","author":"Q Duan","year":"2016","unstructured":"Duan Q, Reid SP, Clark NR, Wang Z, Fernandez NF, Rouillard AD, et al. L1000CDS2: LINCS L1000 characteristic direction signatures search engine. NPJ Syst Biol Appl. 2016. https:\/\/doi.org\/10.1038\/npjsba.2016.15.","journal-title":"NPJ Syst Biol Appl"},{"key":"3915_CR10","doi-asserted-by":"publisher","DOI":"10.1101\/192005","author":"F Napolitano","year":"2017","unstructured":"Napolitano F, Carrella D, Mandriani B, Pisonero S, Sirci F, Medina D, et al. gene2drug: a computational tool for pathway-based rational drug repositioning. Bioinformatics. 2017. https:\/\/doi.org\/10.1101\/192005.","journal-title":"Bioinformatics"},{"key":"3915_CR11","doi-asserted-by":"publisher","first-page":"e9405","DOI":"10.15252\/msb.20199405","volume":"16","author":"E Gon\u00e7alves","year":"2020","unstructured":"Gon\u00e7alves E, Segura-Cabrera A, Pacini C, Picco G, Behan FM, Jaaks P, et al. Drug mechanism-of-action discovery through the integration of pharmacological and CRISPR screens. Mol Syst Biol. 2020;16:e9405.","journal-title":"Mol Syst Biol"},{"key":"3915_CR12","doi-asserted-by":"publisher","first-page":"e1000925","DOI":"10.1371\/journal.pcbi.1000925","volume":"6","author":"M Iskar","year":"2010","unstructured":"Iskar M, Campillos M, Kuhn M, Jensen LJ, van Noort V, Bork P. Drug-induced regulation of target expression. PLoS Comput Biol. 2010;6:e1000925.","journal-title":"PLoS Comput Biol"},{"key":"3915_CR13","doi-asserted-by":"publisher","first-page":"882","DOI":"10.1093\/bioinformatics\/bts034","volume":"28","author":"JT Leek","year":"2012","unstructured":"Leek JT, Johnson WE, Parker HS, Jaffe AE, Storey JD. The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics. 2012;28:882\u20133.","journal-title":"Bioinformatics"},{"key":"3915_CR14","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521:436\u201344.","journal-title":"Nature"},{"key":"3915_CR15","doi-asserted-by":"publisher","first-page":"e1005335","DOI":"10.1371\/journal.pcbi.1005335","volume":"13","author":"TM Filzen","year":"2017","unstructured":"Filzen TM, Kutchukian PS, Hermes JD, Li J, Tudor M. Representing high throughput expression profiles via perturbation barcodes reveals compound targets. PLoS Comput Biol. 2017;13:e1005335.","journal-title":"PLoS Comput Biol"},{"key":"3915_CR16","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-017-07705-8","author":"L Xie","year":"2017","unstructured":"Xie L, He S, Wen Y, Bo X, Zhang Z. Discovery of novel therapeutic properties of drugs from transcriptional responses based on multi-label classification. Sci Rep. 2017. https:\/\/doi.org\/10.1038\/s41598-017-07705-8.","journal-title":"Sci Rep"},{"key":"3915_CR17","doi-asserted-by":"publisher","first-page":"2338","DOI":"10.1093\/bioinformatics\/btw168","volume":"32","author":"Z Wang","year":"2016","unstructured":"Wang Z, Clark NR, Ma\u2019ayan A. Drug-induced adverse events prediction with the LINCS L1000 data. Bioinformatics. 2016;32:2338\u201345.","journal-title":"Bioinformatics"},{"key":"3915_CR18","doi-asserted-by":"publisher","first-page":"4314","DOI":"10.1021\/acs.molpharmaceut.8b00284","volume":"15","author":"Y Donner","year":"2018","unstructured":"Donner Y, Kazmierczak S, Fortney K. Drug repurposing using deep embeddings of gene expression profiles. Mol Pharm. 2018;15:4314\u201325.","journal-title":"Mol Pharm"},{"key":"3915_CR19","doi-asserted-by":"publisher","first-page":"10010","DOI":"10.1093\/nar\/gkz805","volume":"47","author":"B Szalai","year":"2019","unstructured":"Szalai B, Subramanian V, Holland CH, Alf\u00f6ldi R, Pusk\u00e1s LG, Saez-Rodriguez J. Signatures of cell death and proliferation in perturbation transcriptomics data\u2014from confounding factor to effective prediction. Nucleic Acids Res. 2019;47:10010\u201326.","journal-title":"Nucleic Acids Res"},{"issue":"1437\u20131452","key":"3915_CR20","first-page":"e17","volume":"171","author":"A Subramanian","year":"2017","unstructured":"Subramanian A, Narayan R, Corsello SM, Peck DD, Natoli TE, Lu X, et al. A next generation connectivity map: L1000 platform and the first 1,000,000 profiles. Cell. 2017;171(1437\u20131452):e17.","journal-title":"Cell"},{"key":"3915_CR21","doi-asserted-by":"publisher","first-page":"405","DOI":"10.1038\/nm.4306","volume":"23","author":"SM Corsello","year":"2017","unstructured":"Corsello SM, Bittker JA, Liu Z, Gould J, McCarren P, Hirschman JE, et al. The drug repurposing hub: a next-generation drug library and information resource. Nat Med. 2017;23:405\u20138.","journal-title":"Nat Med"},{"key":"3915_CR22","unstructured":"Abadi M, Barham P, Chen J, Chen Z, Davis A, Dean J, et al. TensorFlow: a system for large-scale machine learning. arXiv:1605.08695 [cs]. 2016. Accessed 2 Jul 2020."},{"key":"3915_CR23","doi-asserted-by":"publisher","first-page":"1832","DOI":"10.1093\/bioinformatics\/btw074","volume":"32","author":"Y Chen","year":"2016","unstructured":"Chen Y, Li Y, Narayan R, Subramanian A, Xie X. Gene expression inference with deep learning. Bioinformatics. 2016;32:1832\u20139.","journal-title":"Bioinformatics"},{"key":"3915_CR24","doi-asserted-by":"publisher","first-page":"667","DOI":"10.1186\/s12864-018-5031-0","volume":"19","author":"L Xie","year":"2018","unstructured":"Xie L, He S, Song X, Bo X, Zhang Z. Deep learning-based transcriptome data classification for drug-target interaction prediction. BMC Genom. 2018;19:667.","journal-title":"BMC Genom"},{"key":"3915_CR25","doi-asserted-by":"publisher","first-page":"14621","DOI":"10.1073\/pnas.1000138107","volume":"107","author":"F Iorio","year":"2010","unstructured":"Iorio F, Bosotti R, Scacheri E, Belcastro V, Mithbaokar P, Ferriero R, et al. Discovery of drug mode of action and drug repositioning from transcriptional responses. Proc Natl Acad Sci. 2010;107:14621\u20136.","journal-title":"Proc Natl Acad Sci"},{"key":"3915_CR26","first-page":"2579","volume":"9","author":"L van der Maaten","year":"2008","unstructured":"van der Maaten L, Hinton G. Visualizing data using t-SNE. J Mach Learn Res. 2008;9:2579\u2013605.","journal-title":"J Mach Learn Res"},{"key":"3915_CR27","unstructured":"Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al. Scikit-learn: machine learning in python. arXiv:1201.0490 [cs]. 2012. Accessed 17 Jan 2019."},{"key":"3915_CR28","doi-asserted-by":"publisher","first-page":"2524","DOI":"10.1021\/acs.molpharmaceut.6b00248","volume":"13","author":"A Aliper","year":"2016","unstructured":"Aliper A, Plis S, Artemov A, Ulloa A, Mamoshina P, Zhavoronkov A. Deep learning applications for predicting pharmacological properties of drugs and drug repurposing using transcriptomic data. Mol Pharm. 2016;13:2524\u201330.","journal-title":"Mol Pharm"},{"key":"3915_CR29","doi-asserted-by":"publisher","first-page":"1929","DOI":"10.1126\/science.1132939","volume":"313","author":"J Lamb","year":"2006","unstructured":"Lamb J. The connectivity map: using gene-expression signatures to connect small molecules, genes, and disease. Science. 2006;313:1929\u201335.","journal-title":"Science"},{"key":"3915_CR30","doi-asserted-by":"crossref","unstructured":"Canton H, Verri\u00e8le L, J. Millan M. Competitive antagonism of serotonin (5-HT)2C and 5-HT2A receptormediated phosphoinositide (PI) turnover by clozapine in the rat: a comparison to other antipsychotics. 1994.","DOI":"10.1016\/0304-3940(94)90561-4"},{"key":"3915_CR31","doi-asserted-by":"crossref","unstructured":"Sweet R, Pollock B, H. Mulsant B, Rosen J, Sorisio D, Kirshner M, et al. Pharmacologic profile of Perphenazine\u2019s metabolites. 2000.","DOI":"10.1097\/00004714-200004000-00010"},{"key":"3915_CR32","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1002\/pros.22739","volume":"74","author":"S Ginzburg","year":"2014","unstructured":"Ginzburg S, Golovine KV, Makhov PB, Uzzo RG, Kutikov A, Kolenko VM. Piperlongumine inhibits NF-\u03baB activity and attenuates aggressive growth characteristics of prostate cancer cells: piperlongumine inhibits NF-\u03baB activity. Prostate. 2014;74:177\u201386.","journal-title":"Prostate"},{"key":"3915_CR33","doi-asserted-by":"crossref","unstructured":"Na YJ, Jeon YJ, Suh J-H, Kang JS, Yang K-H, Kim H-M. Suppression of IL-8 gene expression by radicicol is mediated through the inhibition of ERK1r2 and p38 signaling and negative regulation of NF-k B and AP-. 2001;11.","DOI":"10.1016\/S1567-5769(01)00113-8"},{"key":"3915_CR34","doi-asserted-by":"publisher","first-page":"938","DOI":"10.1002\/ajh.20732","volume":"81","author":"A Morotti","year":"2006","unstructured":"Morotti A, Cilloni D, Pautasso M, Messa F, Arruga F, Defilippi I, et al. NF-kB inhibition as a strategy to enhance etoposide-induced apoptosis in K562 cell line. Am J Hematol. 2006;81:938\u201345.","journal-title":"Am J Hematol"},{"key":"3915_CR35","doi-asserted-by":"publisher","DOI":"10.1038\/s41422-020-0282-0","author":"M Wang","year":"2020","unstructured":"Wang M, Cao R, Zhang L, Yang X, Liu J, Xu M, et al. Remdesivir and chloroquine effectively inhibit the recently emerged novel coronavirus (2019-nCoV) in vitro. Cell Res. 2020. https:\/\/doi.org\/10.1038\/s41422-020-0282-0.","journal-title":"Cell Res"},{"key":"3915_CR36","doi-asserted-by":"publisher","first-page":"517","DOI":"10.1056\/NEJMoa2016638","volume":"383","author":"DR Boulware","year":"2020","unstructured":"Boulware DR, Pullen MF, Bangdiwala AS, Pastick KA, Lofgren SM, Okafor EC, et al. A randomized trial of hydroxychloroquine as postexposure prophylaxis for Covid-19. N Engl J Med. 2020;383:517\u201325.","journal-title":"N Engl J Med"},{"key":"3915_CR37","doi-asserted-by":"publisher","first-page":"314","DOI":"10.1007\/978-1-4899-7687-1_192","volume-title":"Encyclopedia of machine learning and data mining","author":"E Keogh","year":"2017","unstructured":"Keogh E, Mueen A. Curse of dimensionality. In: Sammut C, Webb GI, editors. Encyclopedia of machine learning and data mining. Boston: Springer; 2017. p. 314\u20135. https:\/\/doi.org\/10.1007\/978-1-4899-7687-1_192."},{"key":"3915_CR38","doi-asserted-by":"publisher","first-page":"352","DOI":"10.1038\/s41598-017-00535-8","volume":"7","author":"C Lv","year":"2017","unstructured":"Lv C, Wu X, Wang X, Su J, Zeng H, Zhao J, et al. The gene expression profiles in response to 102 traditional Chinese medicine (TCM) components: a general template for research on TCMs. Sci Rep. 2017;7:352.","journal-title":"Sci Rep"},{"key":"3915_CR39","doi-asserted-by":"publisher","first-page":"3057","DOI":"10.1158\/0008-5472.CAN-17-0096","volume":"77","author":"N El-Hachem","year":"2017","unstructured":"El-Hachem N, Gendoo DMA, Ghoraie LS, Safikhani Z, Smirnov P, Chung C, et al. Integrative cancer pharmacogenomics to infer large-scale drug taxonomy. Cancer Res. 2017;77:3057\u201369.","journal-title":"Cancer Res"}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-020-03915-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1186\/s12859-020-03915-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-020-03915-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,1,7]],"date-time":"2021-01-07T12:06:41Z","timestamp":1610021201000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/s12859-020-03915-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,7]]},"references-count":39,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,12]]}},"alternative-id":["3915"],"URL":"https:\/\/doi.org\/10.1186\/s12859-020-03915-6","relation":{},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,7]]},"assertion":[{"value":"2 September 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 November 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 January 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Not applicable.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"17"}}