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BLASSO: integration of biological knowledge into a regularized linear model Urda Muñoz, Daniel Aragón, Francisco Bautista, Rocío Franco, Leonardo Veredas, Francisco J. Claros, Manuel Gonzalo Jerez, José Manuel RNA-Seq Biomarkers selection Biological knowledge Machine learning Precision medicine Informática Biología molecular Computer science Molecular biology In RNA-Seq gene expression analysis, a genetic signature or biomarker is defined as a subset of genes that is probably involved in a given complex human trait and usually provide predictive capabilities for that trait. The discovery of new genetic signatures is challenging, as it entails the analysis of complex-nature information encoded at gene level. Moreover, biomarkers selection becomes unstable, since high correlation among the thousands of genes included in each sample usually exists, thus obtaining very low overlapping rates between the genetic signatures proposed by different authors. In this sense, this paper proposes BLASSO, a simple and highly interpretable linear model with l1-regularization that incorporates prior biological knowledge to the prediction of breast cancer outcomes. Two different approaches to integrate biological knowledge in BLASSO, Gene-specific and Gene-disease, are proposed to test their predictive performance and biomarker stability on a public RNA-Seq gene expression dataset for breast cancer. The relevance of the genetic signature for the model is inspected by a functional analysis. Trabajo presentado en: V International Work-Conference on Bioinformatics and Biomedical Engineering Publication of this manuscript was sponsored by TIN2017-88728-C2 grant. 2023-01-18T08:00:34Z 2023-01-18T08:00:34Z 2018 info:eu-repo/semantics/conferenceObject info:eu-repo/semantics/publishedVersion 1752-0509 http://hdl.handle.net/10259/7263 s12918-018-0612-8 eng BMC Systems Biology. 2018, V. 12, n. 5, p. 14-26 https://doi.org/10.1186/s12918-018-0612-8 info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2017-88728-C2-1-R/ES/AVANCES EN EL DISEÑO Y ADAPTACION DE ALGORITMOS DE APRENDIZAJE PROFUNDO PARA SU APLICACION A PROBLEMAS EN LAS AREAS DE BIOMEDICINA Y CONTAMINACION ATMOSFERICA/ Atribución 4.0 Internacional http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess application/pdf Springer Nature