Section: New Results

Statistical analysis of genomic data

Participant : Kevin Bleakley.

In collaboration with Benno Schwikowski, Iryna Nikolayeva and Anavaj Sakuntabhai (Pasteur Institute, Paris), Kevin Bleakley worked on using 2-d isotonic regression to predict dengue fever severity at hospital arrival using high-dimensional microarray gene expression data. Important marker genes for dengue severity have been detected, some of which now have been validated in external lab trials, and an article on this was published in the Journal of Infectious Diseases in 2018.

Kevin Bleakley has also collaborated with Inserm/Paris-Saclay researchers at Kremlin-Bicêtre hospital on cyclic transcriptional clocks and renal corticosteroid signaling, and has developed novel statistical tests for detecting synchronous signals. This work was published in the FASEB journal in 2018.

Kevin Bleakley worked as part of a consortium on a crowdsourced Dream Challenge in 2018 on using molecular signatures to predict susceptibility to viral infection. Essentially, many teams of researchers from around the world used machine learning (statistical learning) algorithms to learn on training data then test on unseen real data. In the final stage, methods from several teams were combined to improve overall prediction performance. The article “A crowdsourced analysis to identify ab initio molecular signatures predictive of susceptibility to viral infection” was published in Nature Communications in 2018.