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Scalable learning of interpretable rules for the dynamic microbiome domain

Venkata Suhas Maringanti, View ORCID ProfileVanni Bucci, View ORCID ProfileGeorg K. Gerber
doi: https://doi.org/10.1101/2020.06.25.172270
Venkata Suhas Maringanti
1Department of Computer and Information Science, University of Massachusetts Dartmouth, MA, USA
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Vanni Bucci
2Department of Microbiology and Physiological Systems, University of Massachusetts Medical School, Worcester, MA, USA
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  • For correspondence: vanni.bucci2@umassmed.edu ggerber@bwh.harvard.edu
Georg K. Gerber
3Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
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  • ORCID record for Georg K. Gerber
  • For correspondence: vanni.bucci2@umassmed.edu ggerber@bwh.harvard.edu
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Abstract

The microbiome, which is inherently dynamic, plays essential roles in human physiology and its disruption has been implicated in numerous human diseases. Linking dynamic changes in the microbiome to the status of the human host is an important problem, which is complicated by limitations and complexities of the data. Model interpretability is key in the microbiome field, as practitioners seek to derive testable biological hypotheses from data or develop diagnostic tests that can be understood by clinicians. Interpretable structure must take into account domainspecific information key to biologists and clinicians including evolutionary relationships (phylogeny) and dynamic behavior of the microbiome. A Bayesian model was previously developed in the field, which uses Markov Chain Monte Carlo inference to learn human interpretable rules for classifying the status of the human host based on microbiome time-series data, but that approach is not scalable to increasingly large microbiome datasets being produced. We present a new fully-differentiable model that also learns human-interpretable rules for the same classification task, but in an end-to-end gradient-descent based framework. We validate the performance of our model on human microbiome data sets and demonstrate our approach has similar predictive performance to the fully Bayesian method, while running orders-of-magnitude faster and moreover learning a larger set of rules, thus providing additional biological insight into the effects of diet and environment on the microbiome.

Competing Interest Statement

V.B. receives support from a Sponsored Research Agreement from Vedanta Biosciences, Inc. G.K.G. is a Strategic Advisory Board member and shareholder of Kaleido Biosciences, Inc., and a Scientific Advisory Board member and shareholder of ParetoBio, Inc. G.K.G.'s financial interests were reviewed and are managed by Brigham and Women's Hospital and Partners Healthcare in accordance with their conflict of interest policies. The remaining authors declare that they have no competing interests. None of the work in this study was supported by commercial interests.

Footnotes

  • https://github.com/gerberlab/mditre

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-ND 4.0 International license.
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Posted June 28, 2020.
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Scalable learning of interpretable rules for the dynamic microbiome domain
Venkata Suhas Maringanti, Vanni Bucci, Georg K. Gerber
bioRxiv 2020.06.25.172270; doi: https://doi.org/10.1101/2020.06.25.172270
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Scalable learning of interpretable rules for the dynamic microbiome domain
Venkata Suhas Maringanti, Vanni Bucci, Georg K. Gerber
bioRxiv 2020.06.25.172270; doi: https://doi.org/10.1101/2020.06.25.172270

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