iBT-Net: an incremental broad transformer network for cancer drug response prediction

• Published in: Briefings in bioinformatics Vol. 24; no. 4
• Main Authors: Zhan, Yongkang, Guo, Jifeng, Philip Chen, C L, Meng, Xian-Bing
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 20.07.2023; Oxford Publishing Limited (England)
• Subjects: Antineoplastic Agents - pharmacology; Antineoplastic Agents - therapeutic use; Cancer; Cell Line; Drug development; Drugs; Education, Continuing; Gene expression; Humans; Learning; Neoplasms - drug therapy; Neoplasms - genetics; Precision Medicine; Predictions; Relearning; Transformers; Tumor cell lines; Drug Response Prediction; Broad Learning System; Transformer; Incremental Learning
• ISSN: 1467-5463; 1477-4054
• DOI: 10.1093/bib/bbad256

Abstract In modern precision medicine, it is an important research topic to predict cancer drug response. Due to incomplete chemical structures and complex gene features, however, it is an ongoing work to design efficient data-driven methods for predicting drug response. Moreover, since the clinical data cannot be easily obtained all at once, the data-driven methods may require relearning when new data are available, resulting in increased time consumption and cost. To address these issues, an incremental broad Transformer network (iBT-Net) is proposed for cancer drug response prediction. Different from the gene expression features learning from cancer cell lines, structural features are further extracted from drugs by Transformer. Broad learning system is then designed to integrate the learned gene features and structural features of drugs to predict the response. With the capability of incremental learning, the proposed method can further use new data to improve its prediction performance without retraining totally. Experiments and comparison studies demonstrate the effectiveness and superiority of iBT-Net under different experimental configurations and continuous data learning.