Machine learning on DNA-encoded library count data using an uncertainty-aware probabilistic loss function

1 code implementation • 27 Aug 2021 • Katherine S. Lim, Andrew G. Reidenbach, Bruce K. Hua, Jeremy W. Mason, Christopher J. Gerry, Paul A. Clemons, Connor W. Coley

Further, this approach to uncertainty-aware regression is applicable to other sparse or noisy datasets where the nature of stochasticity is known or can be modeled; in particular, the Poisson enrichment ratio metric we use can apply to other settings that compare sequencing count data between two experimental conditions.

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