Anticancer drug synergy prediction in understudied tissues using transfer learning.

ABSTRACT:

Objective

Drug combination screening has advantages in identifying cancer treatment options with higher efficacy without degradation in terms of safety. A key challenge is that the accumulated number of observations in in-vitro drug responses varies greatly among different cancer types, where some tissues are more understudied than the others. Thus, we aim to develop a drug synergy prediction model for understudied tissues as a way of overcoming data scarcity problems.

Materials and methods

We collected a comprehensive set of genetic, molecular, phenotypic features for cancer cell lines. We developed a drug synergy prediction model based on multitask deep neural networks to integrate multimodal input and multiple output. We also utilized transfer learning from data-rich tiss

SUBMITTER: Kim Y 

PROVIDER: S-EPMC7810460 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature