Aguirre Plans, Joaquim, 1993-Piñero González, Janet, 1977-Souza, TerezinhaCallegaro, GiuliaKunnen, Steven J.Sanz, FerranFernández Fuentes, NarcísFurlong, Laura I., 1971-Guney, EmreOliva Miguel, Baldomero2021-02-102021-02-102021Aguirre-Plans J, Piñero J, Souza T, Callegaro G, Kunnen SJ, Sanz F, Fernandez-Fuentes N, Furlong LI, Guney E, Oliva B. An ensemble learning approach for modeling the systems biology of drug-induced injury. Biol Direct. 2021; 16(1):5. DOI: 10.1186/s13062-020-00288-x1745-6150http://hdl.handle.net/10230/46406Background: Drug-induced liver injury (DILI) is an adverse reaction caused by the intake of drugs of common use that produces liver damage. The impact of DILI is estimated to affect around 20 in 100,000 inhabitants worldwide each year. Despite being one of the main causes of liver failure, the pathophysiology and mechanisms of DILI are poorly understood. In the present study, we developed an ensemble learning approach based on different features (CMap gene expression, chemical structures, drug targets) to predict drugs that might cause DILI and gain a better understanding of the mechanisms linked to the adverse reaction. Results: We searched for gene signatures in CMap gene expression data by using two approaches: phenotype-gene associations data from DisGeNET, and a non-parametric test comparing gene expression of DILI-Concern and No-DILI-Concern drugs (as per DILIrank definitions). The average accuracy of the classifiers in both approaches was 69%. We used chemical structures as features, obtaining an accuracy of 65%. The combination of both types of features produced an accuracy around 63%, but improved the independent hold-out test up to 67%. The use of drug-target associations as feature obtained the best accuracy (70%) in the independent hold-out test. Conclusions: When using CMap gene expression data, searching for a specific gene signature among the landmark genes improves the quality of the classifiers, but it is still limited by the intrinsic noise of the dataset. When using chemical structures as a feature, the structural diversity of the known DILI-causing drugs hampers the prediction, which is a similar problem as for the use of gene expression information. The combination of both features did not improve the quality of the classifiers but increased the robustness as shown on independent hold-out tests. The use of drug-target associations as feature improved the prediction, specially the specificity, and the results were comparable to previous research studies.application/pdfeng© The Author(s). 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data mainfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1186/s13062-020-00288-xCAMDACmapDrug safetyDrug structureDrug-induced liver injuryHepatotoxicityMachine learningSystems biologyinfo:eu-repo/semantics/openAccess