Contrastive Multiview Coding for Enzyme-Substrate Interaction Prediction

• URL: http://arxiv.org/abs/2111.09467v1
• Date: Thu, 18 Nov 2021 01:18:36 GMT
• Title: Contrastive Multiview Coding for Enzyme-Substrate Interaction Prediction
• Authors: Apurva Kalia (1), Dilip Krishnan (2), Soha Hassoun (1) ((1) Tufts University, (2) Google Research)
• Abstract summary: We present a novel approach of applying Contrastive Multiview Coding to this problem to improve the performance of prediction. We show that congruency in the multiple views of the reaction data can be used to improve prediction performance.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Characterizing Enzyme function is an important requirement for predicting Enzyme-Substrate interactions. In this paper, we present a novel approach of applying Contrastive Multiview Coding to this problem to improve the performance of prediction. We present a method to leverage auxiliary data from an Enzymatic database like KEGG to learn the mutual information present in multiple views of enzyme-substrate reactions. We show that congruency in the multiple views of the reaction data can be used to improve prediction performance.

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