Journal
BMC BIOINFORMATICS
Volume 20, Issue -, Pages -Publisher
BMC
DOI: 10.1186/s12859-019-2969-0
Keywords
Genomics; Big data; Machine learning; Fanconi anemia; Signaling pathways; Mathematical models
Categories
Funding
- Spanish Ministry of Economy and Competitiveness [SAF2017-88908-R]
- Plataforma de Recursos Biomoleculares y Bioinformaticos from the ISCIII [PT17/0009/0006]
- European Regional Development Funds (ERDF)
- European Union [GA 813533, GA 676559]
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BackgroundIn 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 is especially dramatic in scenarios in which the availability of samples is difficult, such as the case of rare diseases.ResultsThe application of multi-output regression machine learning methodologies to predict the potential effect of external proteins over the signaling circuits that trigger Fanconi anemia related cell functionalities, inferred with a mechanistic model, allowed us to detect over 20 potential therapeutic targets.ConclusionsThe use of artificial intelligence methods for the prediction of potentially causal relationships between proteins of interest and cell activities related with disease-related phenotypes opens promising avenues for the systematic search of new targets in rare diseases.
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