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
