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Combining Mechanistic Models and Machine Intelligence to Investigate the Druggable Region Surrounding the Fanconi Anaemia Pathway

Mohamed Alber

 Given the imbalance between samples and candidate genes, and despite the amount of genomic data, it is challenging to develop predictive models that explain phenotypes as a function of gene expression or mutations. And in situations when sample availability is problematic, like in the case of uncommon diseases, this is more striking. Results we identified over 20 possible treatment targets by using multi-output regression machine learning approaches to estimate the potential impact of exogenous proteins over the signaling circuits that initiate Fanconi anemia-related cell functionalities. The systematic search for new targets in rare diseases is made possible by the use of artificial intelligence tools for the prediction of potentially causative links between proteins of interest and cell activities associated with disease-related phenotypes.