Cancer drug response prediction with surrogate modeling-based graph neural architecture search.

ABSTRACT:

Motivation

Understanding drug-response differences in cancer treatments is one of the most challenging aspects of personalized medicine. Recently, graph neural networks (GNNs) have become state-of-the-art methods in many graph representation learning scenarios in bioinformatics. However, building an optimal handcrafted GNN model for a particular drug sensitivity dataset requires manual design and fine-tuning of the hyperparameters for the GNN model, which is time-consuming and requires expert knowledge.

Results

In this work, we propose AutoCDRP, a novel framework for automated cancer drug-response predictor using GNNs. Our approach leverages surrogate modeling to efficiently search for the most effective GNN architecture. AutoCDRP uses a surrogate model to predict the perfor

SUBMITTER: Oloulade BM 

PROVIDER: S-EPMC10432359 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature