Modeling drug combination effects via latent tensor reconstruction.

ABSTRACT:

Motivation

Combination therapies have emerged as a powerful treatment modality to overcome drug resistance and improve treatment efficacy. However, the number of possible drug combinations increases very rapidly with the number of individual drugs in consideration, which makes the comprehensive experimental screening infeasible in practice. Machine-learning models offer time- and cost-efficient means to aid this process by prioritizing the most effective drug combinations for further pre-clinical and clinical validation. However, the complexity of the underlying interaction patterns across multiple drug doses and in different cellular contexts poses challenges to the predictive modeling of drug combination effects.

Results

We introduce comboLTR, highly time-efficient method

SUBMITTER: Wang T 

PROVIDER: S-EPMC8336593 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature