Integrative random forest for gene regulatory network inference

Motivation: Gene regulatory network (GRN) inference based on genomic data is one of the most actively pursued computational biological problems. Because different types of biological data usually provide complementary information regarding the underlying GRN, a model that integrates big data of dive...

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Detalles Bibliográficos
Autores principales: Petralia, Francesca, Wang, Pei, Yang, Jialiang, Tu, Zhidong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2015
Materias:
Ismb/Eccb 2015 Proceedings Papers Committee July 10 to July 14, 2015, Dublin, Ireland
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4542785/
https://www.ncbi.nlm.nih.gov/pubmed/26072483
http://dx.doi.org/10.1093/bioinformatics/btv268

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4542785/
https://www.ncbi.nlm.nih.gov/pubmed/26072483
http://dx.doi.org/10.1093/bioinformatics/btv268

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