TG468: a text graph convolutional network for predicting clinical response to immune checkpoint inhibitor therapy.

ABSTRACT: Enhancing cancer treatment efficacy remains a significant challenge in human health. Immunotherapy has witnessed considerable success in recent years as a treatment for tumors. However, due to the heterogeneity of diseases, only a fraction of patients exhibit a positive response to immune checkpoint inhibitor (ICI) therapy. Various single-gene-based biomarkers and tumor mutational burden (TMB) have been proposed for predicting clinical responses to ICI; however, their predictive ability is limited. We propose the utilization of the Text Graph Convolutional Network (GCN) method to comprehensively assess the impact of multiple genes, aiming to improve the predictive capability for ICI response. We developed TG468, a Text GCN model framing drug response prediction as a text classification tas

SUBMITTER: Wang K 

PROVIDER: S-EPMC10886443 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature