Unsupervised cell line embedding using pairwise drug response correlation.

ABSTRACT: Human cell line models are essential for understanding diseases and cellular functions. They are particularly emphasized in drug discovery because these models enable the systematic screening of chemical compounds and their effects. However, the heterogeneity in measurement techniques and the fragmented characterization of cell lines in chemical screening and omics data pose significant challenges to their optimal utilization. To address this, we introduce an unsupervised deep learning model based on contrastive learning that integrates heterogeneous drug response screening data into a unified cell line embedding. Utilizing the resulting embedding enhances the performance of drug-cell line-related downstream machine learning tasks to varying degrees. We used drug response data from 1,136 c

SUBMITTER: Kim Y 

PROVIDER: S-EPMC12205321 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature