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A structured observation distribution for generative biological sequence prediction and forecasting

View ORCID ProfileEli N. Weinstein, View ORCID ProfileDebora S. Marks
doi: https://doi.org/10.1101/2020.07.31.231381
Eli N. Weinstein
1 Harvard;
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  • For correspondence: eweinstein@g.harvard.edu
Debora S. Marks
2 Harvard Medical School
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Abstract

Generative probabilistic modeling of biological sequences has widespread existing and potential application across biology and biomedicine, from evolutionary biology to epidemiology to protein design. Many standard sequence analysis methods preprocess data using a multiple sequence alignment (MSA) algorithm, one of the most widely used computational methods in all of science. However, as we show in this article, training generative probabilistic models with MSA preprocessing leads to statistical pathologies in the context of sequence prediction and forecasting. To address these problems, we propose a principled drop-in alternative to MSA preprocessing in the form of a structured observation distribution (the ``MuE" distribution). The MuE is a latent alignment model in which not only the alignment variable but also the regressor sequence can be latent. We prove theoretically that the MuE distribution comprehensively generalizes popular methods for inferring biological sequence alignments, and provide a precise characterization of how such biological models have differed from natural language latent alignment models. We show empirically that models that use the MuE as an observation distribution outperform comparable methods across a variety of datasets, and apply MuE models to a novel problem for generative probabilistic sequence models: forecasting pathogen evolution.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted February 24, 2021.
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A structured observation distribution for generative biological sequence prediction and forecasting
Eli N. Weinstein, Debora S. Marks
bioRxiv 2020.07.31.231381; doi: https://doi.org/10.1101/2020.07.31.231381
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A structured observation distribution for generative biological sequence prediction and forecasting
Eli N. Weinstein, Debora S. Marks
bioRxiv 2020.07.31.231381; doi: https://doi.org/10.1101/2020.07.31.231381

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