{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T04:33:54Z","timestamp":1781843634689,"version":"3.54.5"},"reference-count":53,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,9,15]],"date-time":"2023-09-15T00:00:00Z","timestamp":1694736000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,9,15]],"date-time":"2023-09-15T00:00:00Z","timestamp":1694736000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100000268","name":"Biotechnology and Biological Sciences Research Council","doi-asserted-by":"publisher","award":["BB\/M011194\/1"],"award-info":[{"award-number":["BB\/M011194\/1"]}],"id":[{"id":"10.13039\/501100000268","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007601","name":"Horizon 2020","doi-asserted-by":"publisher","award":["826121"],"award-info":[{"award-number":["826121"]}],"id":[{"id":"10.13039\/501100007601","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Understanding the Mechanism of Action (MoA) of a compound is an often challenging but equally crucial aspect of drug discovery that can help improve both its efficacy and safety. Computational methods to aid MoA elucidation usually either aim to predict direct drug targets, or attempt to understand modulated downstream pathways or signalling proteins. Such methods usually require extensive coding experience and results are often optimised for further computational processing, making them difficult for wet-lab scientists to perform, interpret and draw hypotheses from.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>To address this issue, we in this work present MAVEN (Mechanism of Action Visualisation and Enrichment), an R\/Shiny app which allows for GUI-based prediction of drug targets based on chemical structure, combined with causal reasoning based on causal protein\u2013protein interactions and transcriptomic perturbation signatures. The app computes a systems-level view of the mechanism of action of the input compound. This is visualised as a sub-network linking predicted or known targets to modulated transcription factors via inferred signalling proteins. The tool includes a selection of MSigDB gene set collections to perform pathway enrichment on the resulting network, and also allows for custom gene sets to be uploaded by the researcher. MAVEN is hence a user-friendly, flexible tool for researchers without extensive bioinformatics or cheminformatics knowledge to generate interpretable hypotheses of compound Mechanism of Action.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>\n                      MAVEN is available as a fully open-source tool at\n                      <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/laylagerami\/MAVEN\">https:\/\/github.com\/laylagerami\/MAVEN<\/jats:ext-link>\n                      with options to install in a Docker or Singularity container. Full documentation, including a tutorial on example data, is available at\n                      <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/laylagerami.github.io\/MAVEN\">https:\/\/laylagerami.github.io\/MAVEN<\/jats:ext-link>\n                      .\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12859-023-05416-8","type":"journal-article","created":{"date-parts":[[2023,9,15]],"date-time":"2023-09-15T07:19:04Z","timestamp":1694762344000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["MAVEN: compound mechanism of action analysis and visualisation using transcriptomics and compound structure data in R\/Shiny"],"prefix":"10.1186","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0948-2387","authenticated-orcid":false,"given":"Layla","family":"Hosseini-Gerami","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rosa","family":"Hernansaiz Ballesteros","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anika","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Howard","family":"Broughton","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David Andrew","family":"Collier","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andreas","family":"Bender","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,9,15]]},"reference":[{"key":"5416_CR1","doi-asserted-by":"publisher","DOI":"10.1039\/D1CB00069A","author":"M-A Trapotsi","year":"2021","unstructured":"Trapotsi M-A, Hosseini-Gerami L, Bender A. Computational analyses of mechanism of action (MoA): data. Methods Int RSC Chem Biol. 2021. https:\/\/doi.org\/10.1039\/D1CB00069A.","journal-title":"Methods Int RSC Chem Biol"},{"issue":"6","key":"5416_CR2","doi-asserted-by":"publisher","first-page":"1437","DOI":"10.1016\/j.cell.2017.10.049","volume":"171","author":"A Subramanian","year":"2017","unstructured":"Subramanian A, Narayan R, Corsello SM, Peck DD, Natoli TE, Lu X, Gould J, Davis JF, Tubelli AA, Asiedu JK, Lahr DL, Hirschman JE, Liu Z, Donahue M, Julian B, Khan M, Wadden D, Smith IC, Lam D, Liberzon A, Toder C, Bagul M, Orzechowski M, Enache OM, Piccioni F, Johnson SA, Lyons NJ, Berger AH, Shamji AF, Brooks AN, Vrcic A, Flynn C, Rosains J, Takeda DY, Hu R, Davison D, Lamb J, Ardlie K, Hogstrom L, Greenside P, Gray NS, Clemons PA, Silver S, Wu X, Zhao W-N, Read-Button W, Wu X, Haggarty SJ, Ronco LV, Boehm JS, Schreiber SL, Doench JG, Bittker JA, Root DE, Wong B, Golub TR. A next generation connectivity map: L1000 platform and the first 1,000,000 profiles. Cell. 2017;171(6):1437-1452.e17. https:\/\/doi.org\/10.1016\/j.cell.2017.10.049.","journal-title":"Cell"},{"issue":"24","key":"5416_CR3","doi-asserted-by":"publisher","first-page":"5441","DOI":"10.1039\/C8SC00148K","volume":"9","author":"A Mayr","year":"2018","unstructured":"Mayr A, Klambauer G, Unterthiner T, Steijaert M, Wegner JK, Ceulemans H, Clevert D-A, Hochreiter S. Large-scale comparison of machine learning methods for drug target prediction on ChEMBL. Chem Sci. 2018;9(24):5441\u201351. https:\/\/doi.org\/10.1039\/C8SC00148K.","journal-title":"Chem Sci"},{"issue":"1","key":"5416_CR4","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1186\/s13321-015-0098-y","volume":"7","author":"LH Mervin","year":"2015","unstructured":"Mervin LH, Afzal AM, Drakakis G, Lewis R, Engkvist O, Bender A. Target prediction utilising negative bioactivity data covering large chemical space. J Cheminform. 2015;7(1):51. https:\/\/doi.org\/10.1186\/s13321-015-0098-y.","journal-title":"J Cheminform"},{"key":"5416_CR5","doi-asserted-by":"publisher","first-page":"4538","DOI":"10.1016\/j.csbj.2021.08.011","volume":"19","author":"P Carracedo-Reboredo","year":"2021","unstructured":"Carracedo-Reboredo P, Li\u00f1ares-Blanco J, Rodr\u00edguez-Fern\u00e1ndez N, Cedr\u00f3n F, Novoa FJ, Carballal A, Maojo V, Pazos A, Fernandez-Lozano C. A review on machine learning approaches and trends in drug discovery. Comput Struct Biotechnol J. 2021;19:4538\u201358. https:\/\/doi.org\/10.1016\/j.csbj.2021.08.011.","journal-title":"Comput Struct Biotechnol J"},{"issue":"1","key":"5416_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.21203\/rs.3.rs-1239049\/v1","volume":"24","author":"L Hosseini-Gerami","year":"2023","unstructured":"Hosseini-Gerami L, Higgins IA, Collier DA, Laing E, Evans D, Broughton H, Bender A. Benchmarking causal reasoning algorithms for gene expression-based compound mechanism of action analysis. BMC Bioinform. 2023;24(1):1\u201328. https:\/\/doi.org\/10.21203\/rs.3.rs-1239049\/v1.","journal-title":"BMC Bioinform"},{"issue":"1","key":"5416_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41540-019-0118-z","volume":"5","author":"A Liu","year":"2019","unstructured":"Liu A, Trairatphisan P, Gjerga E, Didangelos A, Barratt J, Saez-Rodriguez J. From expression footprints to causal pathways: contextualizing large signaling networks with CARNIVAL. Npj Syst Biol Appl. 2019;5(1):1\u201310. https:\/\/doi.org\/10.1038\/s41540-019-0118-z.","journal-title":"Npj Syst Biol Appl"},{"issue":"11","key":"5416_CR8","doi-asserted-by":"publisher","first-page":"e27009","DOI":"10.1371\/journal.pone.0027009","volume":"6","author":"AE Enayetallah","year":"2011","unstructured":"Enayetallah AE, Ziemek D, Leininger MT, Randhawa R, Yang J, Manion TB, Mather DE, Zavadoski WJ, Kuhn M, Treadway JL, Etages SA. Modeling the mechanism of action of a dgat1 inhibitor using a causal reasoning platform. PLoS ONE. 2011;6(11):e27009. https:\/\/doi.org\/10.1371\/journal.pone.0027009.","journal-title":"PLoS ONE"},{"key":"5416_CR9","doi-asserted-by":"publisher","first-page":"419","DOI":"10.1186\/1471-2164-11-419","volume":"11","author":"R Kumar","year":"2010","unstructured":"Kumar R, Blakemore SJ, Ellis CE, Petricoin EF, Pratt D, Macoritto M, Matthews AL, Loureiro JJ, Elliston K. Causal reasoning identifies mechanisms of sensitivity for a novel AKT kinase inhibitor, GSK690693. BMC Genomics. 2010;11:419. https:\/\/doi.org\/10.1186\/1471-2164-11-419.","journal-title":"BMC Genomics"},{"issue":"1","key":"5416_CR10","doi-asserted-by":"publisher","first-page":"e9730","DOI":"10.15252\/msb.20209730","volume":"17","author":"A Dugourd","year":"2021","unstructured":"Dugourd A, Kuppe C, Sciacovelli M, Gjerga E, Gabor A, Emdal KB, Vieira V, Bekker-Jensen DB, Kranz J, Bindels EM, Costa AS. Causal integration of multi-omics data with prior knowledge to generate mechanistic hypotheses. Mol Syst Biol. 2021;17(1):e9730. https:\/\/doi.org\/10.15252\/msb.20209730.","journal-title":"Mol Syst Biol"},{"issue":"7","key":"5416_CR11","doi-asserted-by":"publisher","first-page":"2075","DOI":"10.1093\/bioinformatics\/btac055","volume":"38","author":"R Hernansaiz-Ballesteros","year":"2022","unstructured":"Hernansaiz-Ballesteros R, Holland CH, Dugourd A, Saez-Rodriguez J. FUNKI: interactive functional footprint-based analysis of omics data. Bioinformatics. 2022;38(7):2075\u20136. https:\/\/doi.org\/10.1093\/bioinformatics\/btac055.","journal-title":"Bioinformatics"},{"key":"5416_CR12","doi-asserted-by":"publisher","unstructured":"Forrest J, Ralphs T, Vigerske S, LouHafer, Kristjansson B, jpfasano, EdwinStraver, Lubin M, Santos H G, rlougee, Saltzman M. Coin-or\/Cbc: Version 2.9.9, 2018. https:\/\/doi.org\/10.5281\/zenodo.1317566.","DOI":"10.5281\/zenodo.1317566"},{"key":"5416_CR13","unstructured":"IBM. ILOG CPLEX optimization studio. https:\/\/www.ibm.com\/products\/ilog-cplex-optimization-studio (Accessed 2019\u201306\u201317)."},{"issue":"12","key":"5416_CR14","doi-asserted-by":"publisher","first-page":"966","DOI":"10.1038\/nmeth.4077","volume":"13","author":"D T\u00fcrei","year":"2016","unstructured":"T\u00fcrei D, Korcsm\u00e1ros T, Saez-Rodriguez J. OmniPath: guidelines and gateway for literature-curated signaling pathway resources. Nat Methods. 2016;13(12):966\u20137. https:\/\/doi.org\/10.1038\/nmeth.4077.","journal-title":"Nat Methods"},{"issue":"1","key":"5416_CR15","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1038\/s41388-019-0953-9","volume":"39","author":"B Sun","year":"2020","unstructured":"Sun B, Mason S, Wilson RC, Hazard SE, Wang Y, Fang R, Wang Q, Yeh ES, Yang M, Roberts TM, Zhao JJ, Wang Q. Inhibition of the transcriptional kinase CDK7 overcomes therapeutic resistance in HER2-positive breast cancers. Oncogene. 2020;39(1):50\u201363. https:\/\/doi.org\/10.1038\/s41388-019-0953-9.","journal-title":"Oncogene"},{"issue":"6","key":"5416_CR16","doi-asserted-by":"publisher","first-page":"417","DOI":"10.1016\/j.cels.2015.12.004","volume":"1","author":"A Liberzon","year":"2015","unstructured":"Liberzon A, Birger C, Thorvaldsd\u00f3ttir H, Ghandi M, Mesirov JP, Tamayo P. The molecular signatures database (MSigDB) hallmark gene set collection. Cell Syst. 2015;1(6):417\u201325. https:\/\/doi.org\/10.1016\/j.cels.2015.12.004.","journal-title":"Cell Syst"},{"issue":"43","key":"5416_CR17","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, Paulovich A, Pomeroy SL, Golub TR, Lander ES, Mesirov JP. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci. 2005;102(43):15545\u201350. https:\/\/doi.org\/10.1073\/pnas.0506580102.","journal-title":"Proc Natl Acad Sci"},{"issue":"1","key":"5416_CR18","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1038\/75556","volume":"25","author":"M Ashburner","year":"2000","unstructured":"Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, Harris MA, Hill DP, Issel-Tarver L, Kasarskis A, Lewis S, Matese JC, Richardson JE, Ringwald M, Rubin GM, Sherlock G. Gene ontology: tool for the unification of biology. Nat Genet. 2000;25(1):25\u20139. https:\/\/doi.org\/10.1038\/75556.","journal-title":"Nat Genet"},{"key":"5416_CR19","unstructured":"BenderGroup\/PIDGINv4, 2022. https:\/\/github.com\/BenderGroup\/PIDGINv4 (Accessed 2022-05-23)."},{"key":"5416_CR20","doi-asserted-by":"publisher","DOI":"10.1101\/337915","author":"L Garcia-Alonso","year":"2018","unstructured":"Garcia-Alonso L, Ibrahim MM, Turei D, Saez-Rodriguez J. Benchmark and integration of resources for the estimation of human transcription factor activities. bioRxiv. 2018. https:\/\/doi.org\/10.1101\/337915.","journal-title":"bioRxiv"},{"key":"5416_CR21","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-017-02391-6","author":"M Schubert","year":"2018","unstructured":"Schubert M, Klinger B, Kl\u00fcnemann M, Sieber A, Uhlitz F, Sauer S, Garnett MJ, Bl\u00fcthgen N, Saez-Rodriguez J. Perturbation-response genes reveal signaling footprints in cancer gene expression. Nat Commun. 2018. https:\/\/doi.org\/10.1038\/s41467-017-02391-6.","journal-title":"Nat Commun"},{"issue":"D1","key":"5416_CR22","doi-asserted-by":"publisher","first-page":"D701","DOI":"10.1093\/nar\/gkab909","volume":"50","author":"L Csabai","year":"2022","unstructured":"Csabai L, Fazekas D, Kadlecsik T, Szalay-Bek\u0151 M, Boh\u00e1r B, Madgwick M, M\u00f3dos D, \u00d6lbei M, Gul L, Sudhakar P, Kubisch J, Oyeyemi OJ, Liska O, Ari E, Hotzi B, Billes VA, Moln\u00e1r E, F\u00f6ldv\u00e1ri-Nagy L, Cs\u00e1lyi K, Demeter A, P\u00e1pai N, Koltai M, Varga M, Lenti K, Farkas IJ, T\u00fcrei D, Csermely P, Vellai T, Korcsm\u00e1ros T. SignaLink3: a multi-layered resource to uncover tissue-specific signaling networks. Nucleic Acids Res. 2022;50(D1):D701\u20139. https:\/\/doi.org\/10.1093\/nar\/gkab909.","journal-title":"Nucleic Acids Res"},{"issue":"D1","key":"5416_CR23","doi-asserted-by":"publisher","first-page":"D504","DOI":"10.1093\/nar\/gkz949","volume":"48","author":"L Licata","year":"2020","unstructured":"Licata L, Lo Surdo P, Iannuccelli M, Palma A, Micarelli E, Perfetto L, Peluso D, Calderone A, Castagnoli L, Cesareni G. SIGNOR 2.0, the signaling network open resource 2.0: 2019 update. Nucleic Acids Res. 2020;48(D1):D504\u201310. https:\/\/doi.org\/10.1093\/nar\/gkz949.","journal-title":"Nucleic Acids Res"},{"issue":"1","key":"5416_CR24","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1093\/nar\/30.1.207","volume":"30","author":"R Edgar","year":"2002","unstructured":"Edgar R, Domrachev M, Lash AE. Gene expression omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 2002;30(1):207\u201310. https:\/\/doi.org\/10.1093\/nar\/30.1.207.","journal-title":"Nucleic Acids Res"},{"issue":"11","key":"5416_CR25","doi-asserted-by":"publisher","first-page":"2498","DOI":"10.1101\/gr.1239303","volume":"13","author":"P Shannon","year":"2003","unstructured":"Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13(11):2498\u2013504. https:\/\/doi.org\/10.1101\/gr.1239303.","journal-title":"Genome Res"},{"issue":"12","key":"5416_CR26","doi-asserted-by":"publisher","first-page":"1739","DOI":"10.1093\/bioinformatics\/btr260","volume":"27","author":"A Liberzon","year":"2011","unstructured":"Liberzon A, Subramanian A, Pinchback R, Thorvaldsd\u00f3ttir H, Tamayo P, Mesirov JP. Molecular signatures database (MSigDB) 3.0. Bioinformatics. 2011;27(12):1739\u201340. https:\/\/doi.org\/10.1093\/bioinformatics\/btr260.","journal-title":"Bioinformatics"},{"key":"5416_CR27","unstructured":"Solver benchmarks. https:\/\/cran.r-project.org\/web\/packages\/prioritizr\/vignettes\/solver_benchmarks.html (Accessed 2023-02-11)."},{"issue":"D1","key":"5416_CR28","doi-asserted-by":"publisher","first-page":"D1100","DOI":"10.1093\/nar\/gkr777","volume":"40","author":"A Gaulton","year":"2012","unstructured":"Gaulton A, Bellis LJ, Bento AP, Chambers J, Davies M, Hersey A, Light Y, McGlinchey S, Michalovich D, Al-Lazikani B, Overington JP. ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res. 2012;40(D1):D1100\u20137. https:\/\/doi.org\/10.1093\/nar\/gkr777.","journal-title":"Nucleic Acids Res"},{"issue":"D1","key":"5416_CR29","doi-asserted-by":"publisher","first-page":"D1202","DOI":"10.1093\/nar\/gkv951","volume":"44","author":"S Kim","year":"2016","unstructured":"Kim S, Thiessen PA, Bolton EE, Chen J, Fu G, Gindulyte A, Han L, He J, He S, Shoemaker BA, Wang J. PubChem substance and compound databases. Nucleic Acids Res. 2016;44(D1):D1202\u201313. https:\/\/doi.org\/10.1093\/nar\/gkv951.","journal-title":"Nucleic Acids Res"},{"issue":"12","key":"5416_CR30","first-page":"2825","volume":"1","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J. Scikit-learn: machine learning in python. J Mach Learn Res. 2011;1(12):2825\u201330.","journal-title":"J Mach Learn Res"},{"key":"5416_CR31","unstructured":"RDKit: open-source cheminformatics software. https:\/\/www.rdkit.org\/ (Accessed 2020\u201301\u201328)."},{"issue":"1","key":"5416_CR32","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1186\/s13321-015-0085-3","volume":"7","author":"MC Burger","year":"2015","unstructured":"Burger MC. Chem doodle web components: HTML5 toolkit for chemical graphics, interfaces, and informatics. J Cheminformatics. 2015;7(1):35. https:\/\/doi.org\/10.1186\/s13321-015-0085-3.","journal-title":"J Cheminformatics"},{"key":"5416_CR33","unstructured":"Chemdoodle. https:\/\/github.com\/zachcp\/chemdoodle (Accessed 2022\u201305\u201309)."},{"issue":"1","key":"5416_CR34","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1186\/s13321-016-0182-y","volume":"8","author":"N Aniceto","year":"2016","unstructured":"Aniceto N, Freitas AA, Bender A, Ghafourian T. A novel applicability domain technique for mapping predictive reliability across the chemical space of a QSAR: reliability-density neighbourhood. J Cheminformatics. 2016;8(1):69. https:\/\/doi.org\/10.1186\/s13321-016-0182-y.","journal-title":"J Cheminformatics"},{"issue":"8","key":"5416_CR35","doi-asserted-by":"publisher","first-page":"838","DOI":"10.1038\/ng.3593","volume":"48","author":"MJ Alvarez","year":"2016","unstructured":"Alvarez MJ, Shen Y, Giorgi FM, Lachmann A, Ding BB, Ye BH, Califano A. Functional characterization of somatic mutations in cancer using network-based inference of protein activity. Nat Genet. 2016;48(8):838\u201347. https:\/\/doi.org\/10.1038\/ng.3593.","journal-title":"Nat Genet"},{"issue":"1","key":"5416_CR36","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1186\/s13059-020-1949-z","volume":"21","author":"CH Holland","year":"2020","unstructured":"Holland CH, Tanevski J, Perales-Pat\u00f3n J, Gleixner J, Kumar MP, Mereu E, Joughin BA, Stegle O, Lauffenburger DA, Heyn H, Szalai B, Saez-Rodriguez J. Robustness and applicability of transcription factor and pathway analysis tools on single-cell RNA-seq data. Genome Biol. 2020;21(1):36. https:\/\/doi.org\/10.1186\/s13059-020-1949-z.","journal-title":"Genome Biol"},{"key":"5416_CR37","unstructured":"Michel Berkelaar. LpSolve: Interface to \u201cLp_solve\u201d v. 5.5 to solve linear\/integer programs, 2022. https:\/\/CRAN.R-project.org\/package=lpSolve (Accessed 2022-09-13)."},{"issue":"D1","key":"5416_CR38","doi-asserted-by":"publisher","first-page":"D649","DOI":"10.1093\/nar\/gkx1132","volume":"46","author":"A Fabregat","year":"2018","unstructured":"Fabregat A, Jupe S, Matthews L, Sidiropoulos K, Gillespie M, Garapati P, Haw R, Jassal B, Korninger F, May B, Milacic M, Roca CD, Rothfels K, Sevilla C, Shamovsky V, Shorser S, Varusai T, Viteri G, Weiser J, Wu G, Stein L, Hermjakob H, D\u2019Eustachio P. The reactome pathway knowledgebase. Nucleic Acids Res. 2018;46(D1):D649\u201355. https:\/\/doi.org\/10.1093\/nar\/gkx1132.","journal-title":"Nucleic Acids Res"},{"issue":"D1","key":"5416_CR39","doi-asserted-by":"publisher","first-page":"D661","DOI":"10.1093\/nar\/gkx1064","volume":"46","author":"DN Slenter","year":"2018","unstructured":"Slenter DN, Kutmon M, Hanspers K, Riutta A, Windsor J, Nunes N, M\u00e9lius J, Cirillo E, Coort SL, Digles D, Ehrhart F, Giesbertz P, Kalafati M, Martens M, Miller R, Nishida K, Rieswijk L, Waagmeester A, Eijssen LMT, Evelo CT, Pico AR, Willighagen EL. WikiPathways: a multifaceted pathway database bridging metabolomics to other omics research. Nucleic Acids Res. 2018;46(D1):D661\u20137. https:\/\/doi.org\/10.1093\/nar\/gkx1064.","journal-title":"Nucleic Acids Res"},{"issue":"8","key":"5416_CR40","doi-asserted-by":"publisher","first-page":"4378","DOI":"10.1093\/nar\/gkt111","volume":"41","author":"L V\u00e4remo","year":"2013","unstructured":"V\u00e4remo L, Nielsen J, Nookaew I. Enriching the gene set analysis of genome-wide data by incorporating directionality of gene expression and combining statistical hypotheses and methods. Nucleic Acids Res. 2013;41(8):4378\u201391. https:\/\/doi.org\/10.1093\/nar\/gkt111.","journal-title":"Nucleic Acids Res"},{"issue":"9","key":"5416_CR41","first-page":"2531","volume":"5","author":"M Segovia-Mendoza","year":"2015","unstructured":"Segovia-Mendoza M, Gonz\u00e1lez-Gonz\u00e1lez ME, Barrera D, D\u00edaz L, Garc\u00eda-Becerra R. Efficacy and mechanism of action of the tyrosine kinase inhibitors Gefitinib, Lapatinib and Neratinib in the treatment of HER2-positive breast cancer: preclinical and clinical evidence. Am J Cancer Res. 2015;5(9):2531\u201361.","journal-title":"Am J Cancer Res"},{"key":"5416_CR42","unstructured":"Riester L W and M HGNChelper: identify and correct invalid HGNC human gene symbols and MGI mouse gene symbols, 2019. https:\/\/CRAN.R-project.org\/package=HGNChelper (Accessed 2022\u201309\u201308)."},{"key":"5416_CR43","doi-asserted-by":"publisher","unstructured":"Jasial S, Hu Y, Vogt M, Bajorath J. Activity-relevant similarity values for fingerprints and implications for similarity searching. F1000Research 2016, https:\/\/doi.org\/10.12688\/f1000research.8357.2.","DOI":"10.12688\/f1000research.8357.2"},{"issue":"4","key":"5416_CR44","doi-asserted-by":"publisher","first-page":"648","DOI":"10.4161\/15384101.2014.994966","volume":"14","author":"K Canfield","year":"2015","unstructured":"Canfield K, Li J, Wilkins OM, Morrison MM, Ung M, Wells W, Williams CR, Liby KT, Vullhorst D, Buonanno A, Hu H, Schiff R, Cook RS, Kurokawa M. Receptor tyrosine kinase ERBB4 mediates acquired resistance to ERBB2 inhibitors in breast cancer cells. Cell Cycle. 2015;14(4):648\u201355. https:\/\/doi.org\/10.4161\/15384101.2014.994966.","journal-title":"Cell Cycle"},{"issue":"8","key":"5416_CR45","doi-asserted-by":"publisher","first-page":"1426","DOI":"10.1016\/j.clinthera.2008.08.008","volume":"30","author":"PJ Medina","year":"2008","unstructured":"Medina PJ, Goodin S. Lapatinib: a dual inhibitor of human epidermal growth factor receptor tyrosine kinases. Clin Ther. 2008;30(8):1426\u201347. https:\/\/doi.org\/10.1016\/j.clinthera.2008.08.008.","journal-title":"Clin Ther"},{"issue":"4","key":"5416_CR46","doi-asserted-by":"publisher","first-page":"472","DOI":"10.1016\/j.ccell.2015.09.005","volume":"28","author":"S Matkar","year":"2015","unstructured":"Matkar S, Sharma P, Gao S, Gurung B, Katona BW, Liao J, Muhammad AB, Kong X-C, Wang L, Jin G, Dang CV, Hua X. An epigenetic pathway regulates sensitivity of breast cancer cells to HER2 inhibition via FOXO\/c-Myc axis. Cancer Cell. 2015;28(4):472\u201385. https:\/\/doi.org\/10.1016\/j.ccell.2015.09.005.","journal-title":"Cancer Cell"},{"issue":"1","key":"5416_CR47","doi-asserted-by":"publisher","first-page":"12156","DOI":"10.1038\/ncomms12156","volume":"7","author":"G Deblois","year":"2016","unstructured":"Deblois G, Smith HW, Tam IS, Gravel S-P, Caron M, Savage P, Labb\u00e9 DP, B\u00e9gin LR, Tremblay ML, Park M, Bourque G, St-Pierre J, Muller WJ, Gigu\u00e8re V. ERR\u03b1 mediates metabolic adaptations driving lapatinib resistance in breast cancer. Nat Commun. 2016;7(1):12156. https:\/\/doi.org\/10.1038\/ncomms12156.","journal-title":"Nat Commun"},{"issue":"12","key":"5416_CR48","doi-asserted-by":"publisher","first-page":"1741","DOI":"10.1074\/mcp.M112.019919","volume":"11","author":"K Imami","year":"2012","unstructured":"Imami K, Sugiyama N, Imamura H, Wakabayashi M, Tomita M, Taniguchi M, Ueno T, Toi M, Ishihama Y. Temporal profiling of lapatinib-suppressed phosphorylation signals in EGFR\/HER2 pathways. Mol Cell Proteomics MCP. 2012;11(12):1741\u201357. https:\/\/doi.org\/10.1074\/mcp.M112.019919.","journal-title":"Mol Cell Proteomics MCP"},{"issue":"4","key":"5416_CR49","doi-asserted-by":"publisher","first-page":"R76","DOI":"10.1186\/bcr3695","volume":"16","author":"LG Est\u00e9vez","year":"2014","unstructured":"Est\u00e9vez LG, Suarez-Gauthier A, Garc\u00eda E, Mir\u00f3 C, Calvo I, Fern\u00e1ndez-Abad M, Herrero M, Marcos M, M\u00e1rquez C, Lopez R\u00edos F, Perea S, Hidalgo M. Molecular effects of lapatinib in patients with HER2 positive ductal carcinoma in situ. Breast Cancer Res. 2014;16(4):R76. https:\/\/doi.org\/10.1186\/bcr3695.","journal-title":"Breast Cancer Res"},{"issue":"12","key":"5416_CR50","doi-asserted-by":"publisher","first-page":"5021","DOI":"10.1073\/pnas.1016140108","volume":"108","author":"JT Garrett","year":"2011","unstructured":"Garrett JT, Olivares MG, Rinehart C, Granja-Ingram ND, S\u00e1nchez V, Chakrabarty A, Dave B, Cook RS, Pao W, McKinely E, Manning HC, Chang J, Arteaga CL. Transcriptional and posttranslational up-regulation of HER3 (ErbB3) compensates for inhibition of the HER2 tyrosine kinase. Proc Natl Acad Sci. 2011;108(12):5021\u20136. https:\/\/doi.org\/10.1073\/pnas.1016140108.","journal-title":"Proc Natl Acad Sci"},{"issue":"3","key":"5416_CR51","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1089\/152791601750294344","volume":"2","author":"D Nishimura","year":"2001","unstructured":"Nishimura D. BioCarta. Biotech Softw Internet Rep. 2001;2(3):117\u201320. https:\/\/doi.org\/10.1089\/152791601750294344.","journal-title":"Biotech Softw Internet Rep"},{"issue":"11","key":"5416_CR52","doi-asserted-by":"publisher","first-page":"999","DOI":"10.1093\/jjco\/hyq084","volume":"40","author":"C Vogel","year":"2010","unstructured":"Vogel C, Chan A, Gril B, Kim S-B, Kurebayashi J, Liu L, Lu Y-S, Moon H. Management of ErbB2-positive breast cancer: insights from preclinical and clinical studies with Lapatinib. Jpn J Clin Oncol. 2010;40(11):999\u20131013. https:\/\/doi.org\/10.1093\/jjco\/hyq084.","journal-title":"Jpn J Clin Oncol"},{"issue":"8","key":"5416_CR53","doi-asserted-by":"publisher","first-page":"E1067","DOI":"10.3390\/cancers11081067","volume":"11","author":"Z Mahmud","year":"2019","unstructured":"Mahmud Z, Gomes AR, Lee HJ, Aimjongjun S, Jiramongkol Y, Yao S, Zona S, Alasiri G, Gong G, Yag\u00fce E, Lam EW-F. EP300 and SIRT1\/6 Co-regulate Lapatinib sensitivity via modulating FOXO3-acetylation and activity in breast cancer. Cancers. 2019;11(8):E1067. https:\/\/doi.org\/10.3390\/cancers11081067.","journal-title":"Cancers"}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-023-05416-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12859-023-05416-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-023-05416-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,19]],"date-time":"2023-11-19T01:05:04Z","timestamp":1700355904000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/s12859-023-05416-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,15]]},"references-count":53,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["5416"],"URL":"https:\/\/doi.org\/10.1186\/s12859-023-05416-8","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2022.07.20.500792","asserted-by":"object"}]},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,15]]},"assertion":[{"value":"27 September 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 July 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 September 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"R.H.B. has received consultant fees from QuantBio. H.B and D.A.C are\/were both employees of Eli Lilly and Company. L.H.G was partially funded by Eli Lilly and Company\u00a0and is now an employee of Ignota Labs. A.L. was funded by GSK and is now an employee of Boehringer Ingelheim.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"344"}}