Crossfeat: a transformer-based cross-feature learning model for predicting drug side effect frequency.

ABSTRACT:

Background

Safe drug treatment requires an understanding of the potential side effects. Identifying the frequency of drug side effects can reduce the risks associated with drug use. However, existing computational methods for predicting drug side effect frequencies heavily depend on known drug side effect frequency information. Consequently, these methods face challenges when predicting the side effect frequencies of new drugs. Although a few methods can predict the side effect frequencies of new drugs, they exhibit unreliable performance owing to the exclusion of drug-side effect relationships.

Results

This study proposed CrossFeat, a model based on convolutional neural network-transformer architecture with cross-feature learning that can predict the occurrence and frequenc

SUBMITTER: Baek B 

PROVIDER: S-EPMC11459996 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature