2026-05-09T00:16:21Zhttps://riubu.ubu.es/oai/requestoai:riubu.ubu.es:10259/72632023-03-17T11:54:10Zcom_10259_3847com_10259_5086com_10259_2604col_10259_7109
Urda Muñoz, Daniel
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600
0000-0003-2662-798X
Aragón, Francisco
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600
Bautista, Rocío
1d520f13-a55a-4aaf-8500-e6c3f5ab4b26
600
Franco, Leonardo
aa73a6ad-a4bd-4742-8bc6-75f6c1ec95bc
600
Veredas, Francisco J.
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600
Claros, Manuel Gonzalo
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Jerez, José Manuel
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2023-01-18T08:00:34Z
2023-01-18T08:00:34Z
2018
1752-0509
http://hdl.handle.net/10259/7263
s12918-018-0612-8
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.
application/pdf
eng
Springer Nature
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
RNA-Seq
Biomarkers selection
Biological knowledge
Machine learning
Precision medicine
Informática
Biología molecular
Computer science
Molecular biology
BLASSO: integration of biological knowledge into a regularized linear model
info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
BMC Systems Biology
12
5
14
26
THUMBNAIL
Urda-bmc_2018.pdf.jpg
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Urda-bmc_2018.pdf
Urda-bmc_2018.pdf
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10259/7263
oai:riubu.ubu.es:10259/7263
2023-03-17 12:54:10.33
Repositorio Institucional de la Universidad de Burgos
bubrep@ubu.es
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