MCLRP: enhanced prediction of anticancer drug response through low-rank matrix completion and transcriptomic profiling.

ABSTRACT:

Background

Accurate prediction of anticancer drug responses remains a significant challenge due to the intricate interplay between genomic features and pharmacological mechanisms. We present Matrix Completion with Low-rank Regularization and Principal Component Analysis (MCLRP), a multimodal framework that synergistically integrates low-rank matrix completion with transcriptomic principal component analysis through dual-stream feature interaction. This innovative architecture not only leverages the similarities among drugs and mutation patterns in cell lines via matrix completion but also preserves gene-level interpretability of response patterns by incorporating gene expression data into the model.

Results

Benchmarked against seven computational paradigms (including matrix

SUBMITTER: Wang K 

PROVIDER: S-EPMC12781783 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature