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CSI: Contrastive data Stratification for Interaction prediction and its application to compound-protein interaction

Apurva Kalia1, Dilip Krishnan2, Soha Hassoun1,3

  • 1Department of Computer Science, Tufts University, Medford, MA 02155, United States.

Bioinformatics (Oxford, England)
|July 25, 2023
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Summary

We introduce Contrastive Stratification for Interaction Prediction (CSI), a novel method for partitioning data to improve interaction prediction. CSI enhances deep learning models by creating multi-views for contrastive learning, significantly boosting prediction accuracy in areas like drug discovery.

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Area of Science:

  • Computational Biology
  • Machine Learning
  • Bioinformatics

Background:

  • Accurate prediction of interactions between biological entities (e.g., compound-protein) is crucial for drug discovery and synthetic biology.
  • Current deep learning models often struggle to fully leverage the relational information inherent in interaction datasets.
  • Exploiting multi-view representations of interacting objects can enhance model performance through contrastive learning.

Purpose of the Study:

  • To develop a novel method, Contrastive Stratification for Interaction Prediction (CSI), for partitioning interaction datasets.
  • To improve the learning of object representations by utilizing congruent and non-congruent data views via contrastive learning.
  • To apply CSI to the compound-protein interaction prediction problem to accelerate drug discovery and related applications.

Main Methods:

  • CSI stratifies (partitions) datasets by assigning a key and multiple views to each data point.
  • Data partitions under a specific key form congruent views, enabling contrastive multiview coding.
  • The method learns embeddings that maximize mutual information across these congruent views.

Main Results:

  • CSI significantly improved average precision in compound-protein interaction prediction, with gains ranging from 13.7% to 39% when using compounds/sequences as keys.
  • Further gains of 16.9% to 63% were observed when using reaction features as keys in enzymatic datasets.
  • These results demonstrate the effectiveness of data stratification and contrastive learning for interaction prediction.

Conclusions:

  • CSI offers a powerful approach to enhance interaction prediction by effectively leveraging multi-view data representations.
  • The method shows substantial improvements over baseline models lacking data stratification and contrastive learning.
  • CSI has the potential to expedite drug discovery, metabolic engineering, and synthetic biology applications.