Complementary feature selection from alternative splicing events and gene expression for phenotype prediction

MOTIVATION: A central task of bioinformatics is to develop sensitive and specific means of providing medical prognoses from biomarker patterns. Common methods to predict phenotypes in RNA-Seq datasets utilize machine learning algorithms trained via gene expression. Isoforms, however, generated from...

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Detalles Bibliográficos
Autores principales: Labuzzetta, Charles J, Antonio, Margaret L, Watson, Patricia M, Wilson, Robert C, Laboissonniere, Lauren A, Trimarchi, Jeffrey M, Genc, Baris, Ozdinler, P Hande, Watson, Dennis K, Anderson, Paul E
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2016
Materias:
ECCB 2016: The 15th European Conference on Computational Biology
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6276944/
https://www.ncbi.nlm.nih.gov/pubmed/27587658
http://dx.doi.org/10.1093/bioinformatics/btw430

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6276944/
https://www.ncbi.nlm.nih.gov/pubmed/27587658
http://dx.doi.org/10.1093/bioinformatics/btw430

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