Development of a deep learning approach to predict noisy biological phenotypes

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Machine learning has long been used to predict phenotypes in biological research, such as cells' growth rate in different media, differences of cells' shape or size, or viability under chemical or radiation perturbations. These phenotypes are often the results of complex experimental protocols generating data that are intrinsically noisy. While current machine learning methods account for the inevitable noise in the input measurements -- gene expression profiling or protein staining, for instance -- there are no established methods to efficiently deal with noisy output variables (phenotypes). This gap represents an opportunity to improve the applicability of machine learning in biological research by accounting for the noisy nature of biological phenotypic measurements. This research program focuses on developing new machine learning methodologies to predict noisy phenotypes common in biological research. I propose to develop a new statistic to better identify associations between molecular features and biological phenotypes. I will then use this statistic to implement a neural network able to maximize it in order to develop nonlinear, multivariate predictors for the phenotypes of interest. I will showcase the utility of our new computational methods for predicting cells' response to chemical perturbation in a large compendium of datasets that we curated during the last 5 years. Finally, my team will create software tools to allow scientists and companies to leverage our new methods in the cloud, therefore maximizing the dissemination and impact of our research program. The methods developed in our research program are not limited to biological data but can be applied to any field where continuous, noisy phenotypes are measured. These include engineering, climate modelling, and physics for instance. By abiding by the principles of open science, i.e., sharing data, methods, documented code and open-source software tools, we hope to create a platform for other scientists to test new and compare new methods and open new avenues of research in diverse fields where learning from noisy measurements is commonplace.