RT info:eu-repo/semantics/conferenceObject
T1 BLASSO: integration of biological knowledge into a regularized linear model
A1 Urda Muñoz, Daniel
A1 Aragón, Francisco
A1 Bautista, Rocío
A1 Franco, Leonardo
A1 Veredas, Francisco J.
A1 Claros, Manuel Gonzalo
A1 Jerez, José Manuel
K1 RNA-Seq
K1 Biomarkers selection
K1 Biological knowledge
K1 Machine learning
K1 Precision medicine
K1 Informática
K1 Computer science
K1 Biología molecular
K1 Molecular biology
AB In RNA-Seq gene expression analysis, a genetic signature or biomarker is defined as a subset of genesthat is probably involved in a given complex human trait and usually provide predictive capabilities for that trait. Thediscovery of new genetic signatures is challenging, as it entails the analysis of complex-nature information encoded atgene level. Moreover, biomarkers selection becomes unstable, since high correlation among the thousands of genesincluded in each sample usually exists, thus obtaining very low overlapping rates between the genetic signaturesproposed by different authors. In this sense, this paper proposes BLASSO, a simple and highly interpretable linearmodel with l1-regularization that incorporates prior biological knowledge to the prediction of breast canceroutcomes. Two different approaches to integrate biological knowledge in BLASSO, Gene-specific and Gene-disease, areproposed to test their predictive performance and biomarker stability on a public RNA-Seq gene expression datasetfor breast cancer. The relevance of the genetic signature for the model is inspected by a functional analysis.
PB Springer Nature
SN 1752-0509
YR 2018
FD 2018
LK http://hdl.handle.net/10259/7263
UL http://hdl.handle.net/10259/7263
LA eng
NO Publication of this manuscript was sponsored by TIN2017-88728-C2 grant.
DS Repositorio Institucional de la Universidad de Burgos
RD 04-dic-2024