Exploring the druggable space around the Fanconi anemia pathway using machine learning and mechanistic models

Exploring the druggable space around the Fanconi anemia pathway using machine learning and mechanistic models

Abstract Background In spite of the abundance of genomic data, predictive models that describe phenotypes as a function of gene expression or mutations are difficult to obtain because they are affected by the curse of dimensionality, given the disbalance between samples and candidate genes. And this...

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Bibliographic Details
Main Authors: Marina Esteban-Medina, María Peña-Chilet, Carlos Loucera, Joaquín Dopazo
Format: Article
Language:English
Published: BMC 2019-07-01
Series:BMC Bioinformatics
Subjects:
Genomics
Big data
Machine learning
Fanconi anemia
Signaling pathways
Mathematical models
Online Access:http://link.springer.com/article/10.1186/s12859-019-2969-0

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http://link.springer.com/article/10.1186/s12859-019-2969-0

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