DualGCN: a dual graph convolutional network model to predict cancer drug response.

ABSTRACT:

Background

Drug resistance is a critical obstacle in cancer therapy. Discovering cancer drug response is important to improve anti-cancer drug treatment and guide anti-cancer drug design. Abundant genomic and drug response resources of cancer cell lines provide unprecedented opportunities for such study. However, cancer cell lines cannot fully reflect heterogeneous tumor microenvironments. Transferring knowledge studied from in vitro cell lines to single-cell and clinical data will be a promising direction to better understand drug resistance. Most current studies include single nucleotide variants (SNV) as features and focus on improving predictive ability of cancer drug response on cell lines. However, obtaining accurate SNVs from clinical tumor samples and single-cell data is no

SUBMITTER: Ma T 

PROVIDER: S-EPMC9011932 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature