School of Computer Science & Engineering,South China University of Technology, 510006, China.
Brain and Affective Cognitive Research Center, Pazhou Lab, 510335, China.
Brief Bioinform. 2023 Jul 20;24(4). doi: 10.1093/bib/bbad256.
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.
在现代精准医学中，预测癌症药物反应是一个重要的研究课题。然而，由于化学结构不完整和基因特征复杂，设计高效的数据驱动方法来预测药物反应仍然是一项正在进行的工作。此外，由于临床数据不能一次性轻易获得，当有新的数据时，数据驱动的方法可能需要重新学习，从而增加了时间和成本的消耗。为了解决这些问题，提出了一种用于癌症药物反应预测的增量式广泛 Transformer 网络（iBT-Net）。与从癌细胞系中学习基因表达特征不同，Transformer 进一步从药物中提取结构特征。然后设计了广泛学习系统来整合学习到的基因特征和药物的结构特征，以预测反应。通过增量学习的能力，所提出的方法可以进一步使用新数据来提高其预测性能，而无需完全重新训练。实验和比较研究证明了 iBT-Net 在不同的实验配置和连续数据学习下的有效性和优越性。
