Interpretable Machine Learning Approaches Applied to Omics Datasets

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In recent decades, advances in sequencing technologies have set off a revolution resulting in an explosion of genetic data, propelling human genomics into the era of big data. Even more recently, novel biotechnologies allow us to obtain information at the molecular level for each individual, such as the concentrations of metabolites (small molecules such as antioxidants, vitamins) or the quantification of RNA transcripts and proteins. These so-called `omics' data sets, combined with genetics established at birth, hold the promise to reveal the molecular causes of the differences between humans, for traits such as height, weight or risk of developing disease. In parallel, recent advances in artificial intelligence have led to the development of powerful methods for making predictions from large data sets, in several areas from autonomous driving to natural language processing. However, machine learning technologies on individual omics signatures are lagging behind, because several challenges still need to be addressed. First, these "black box" methods produce predictions without providing interpretations, preventing experts from getting the evidence necessary to validate the results. Second, current methods tend to learn the datasets "by heart" instead of extracting general knowledge from them. Indeed, although our datasets are large, they contain far fewer participants than variables measured for each participant, which reduces the ability to generalize. Finally, the sources of biological and technical variation, generally inconsistent from one dataset to another, must be considered to obtain reliable predictions in the real world. My research program offers concrete solutions to make these methodologies applicable to omics data, for specific biological problems. One of them aims to predict, from an individual's genetics, changes in the levels of RNA transcripts and metabolites. Another problem targets the prediction of the risk of developing a complex disease based on an individual's omics data. For these concrete applications, we will use omics data from several biobanks, including local (Montreal Heart Institute Biobank), national (CanPath cohort) and international (UK Biobank) cohorts. We will develop these methodologies while ensuring to obtain interpretable and plausible results, which generalize well, and are independent of the noise sources. Our program is interdisciplinary and offers a rich training opportunity for students. Our results will have the potential to improve the appropriate use of machine learning in molecular biology research, providing researchers and Canadian industry with robust tools for omics data analysis that can be interpreted by humans.