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

ABSTRACT:

Motivation

Accurately predicting the likelihood of interaction between two objects (compound-protein sequence, user-item, author-paper, etc.) is a fundamental problem in Computer Science. Current deep-learning models rely on learning accurate representations of the interacting objects. Importantly, relationships between the interacting objects, or features of the interaction, offer an opportunity to partition the data to create multi-views of the interacting objects. The resulting congruent and non-congruent views can then be exploited via contrastive learning techniques to learn enhanced representations of the objects.

Results

We present a novel method, Contrastive Stratification for Interaction Prediction (CSI), to stratify (partition) a dataset in a manner that can be exp

SUBMITTER: Kalia A 

PROVIDER: S-EPMC10423023 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature