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Critiquing Protein Family Classification Models Using Sufficient Input Subsets

View ORCID ProfileBrandon Carter, View ORCID ProfileMaxwell Bileschi, Jamie Smith, View ORCID ProfileTheo Sanderson, Drew Bryant, View ORCID ProfileDavid Belanger, View ORCID ProfileLucy Colwell
doi: https://doi.org/10.1101/674119
Brandon Carter
1MIT Computer Science & Artificial Intelligence Laboratory, Cambridge, MA, USA
2Google Research, Cambridge, MA, USA
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  • For correspondence: bcarter@csail.mit.edu
Maxwell Bileschi
2Google Research, Cambridge, MA, USA
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Jamie Smith
2Google Research, Cambridge, MA, USA
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Theo Sanderson
2Google Research, Cambridge, MA, USA
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Drew Bryant
2Google Research, Cambridge, MA, USA
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David Belanger
2Google Research, Cambridge, MA, USA
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Lucy Colwell
2Google Research, Cambridge, MA, USA
3Dept. of Chemistry, Cambridge University, Cambridge, UK
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Abstract

In many application domains, neural networks are highly accurate and have been deployed at large scale. However, users often do not have good tools for understanding how these models arrive at their predictions. This has hindered adoption in fields such as the life and medical sciences, where researchers require that models base their decisions on underlying biological phenomena rather than peculiarities of the dataset introduced. In response, we propose a set of methods for critiquing deep learning models and demonstrate their application for protein family classification, a task for which high-accuracy models have considerable potential impact. Our methods extend the sufficient input subsets technique, which we use to identify subsets of features (SIS) in each protein sequence that are alone sufficient for classification. Our suite of tools analyzes these subsets to shed light on the decision-making criteria employed by models trained on this task. These tools expose that while deep models may perform classification for biologically-relevant reasons, their behavior varies considerably across choice of network architecture and parameter initialization. While the techniques that we develop are specific to the protein sequence classification task, the approach taken generalizes to a broad set of scientific contexts in which model interpretability is essential.

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Posted June 19, 2019.
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Critiquing Protein Family Classification Models Using Sufficient Input Subsets
Brandon Carter, Maxwell Bileschi, Jamie Smith, Theo Sanderson, Drew Bryant, David Belanger, Lucy Colwell
bioRxiv 674119; doi: https://doi.org/10.1101/674119
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Critiquing Protein Family Classification Models Using Sufficient Input Subsets
Brandon Carter, Maxwell Bileschi, Jamie Smith, Theo Sanderson, Drew Bryant, David Belanger, Lucy Colwell
bioRxiv 674119; doi: https://doi.org/10.1101/674119

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