University of Cambridge > Talks.cam > Machine learning in Physics, Chemistry and Materials discussion group (MLDG) > Towards explainable computational biology: small steps and many questions

Towards explainable computational biology: small steps and many questions

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Large, high capacity, deep learning algorithms and models, trained on large amounts of data have shown to achieve impressive performance and generalize well. As newer, cheaper types of data querying biological systems are currently more available than ever, the popularity of deep learning in computational biology has increased. However, with deeper models comes greater responsibility. Is the complexity of such models warranted and can they bring new insight into scientific decision-making? In this talk, I will survey a number of challenges in computational biology, with a focus on the problem of interpretable feature selection in spatial single cell RNA sequencing data. We will discuss linear approaches to select relevant features using lasso, and extensions to the context of deep learning using variational autoencoders and the famous gumbel softmax trick. This is joint work with Nabeel Sarwar and Soledad ViIllar.

This talk is part of the Machine learning in Physics, Chemistry and Materials discussion group (MLDG) series.

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