Lian Linghang, Yang Xuhua
College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, Zhejiang, China.
Sheng Wu Gong Cheng Xue Bao. 2024 Jul 25;40(7):2235-2245. doi: 10.13345/j.cjb.230902.
In recent years, precision medicine has demonstrated wide applications in cancer therapy, and the focus of precision medicine lies in accurately predicting the responses of different patients to drug treatment. We propose a model for predicting cancer drug sensitivity based on genomic feature distribution alignment and drug structure information. This model initially aligns the genomic features from cell lines with those from patients and removes noise from gene expression data. Subsequently, it integrates drug structure features and employs multi-task learning to predict the drug sensitivity of patients. The experimental results on the genomics of drug sensitivity in cancer (GDSC) dataset indicates that this method achieved a reduced mean square error of 0.905 2, an increased correlation coefficient of 0.875 4, and an enhanced accuracy rate of 0.836 0 which significantly outperformed the recently published methods. On the cancer genome atlas (TCGA) dataset, this method demonstrates an improved average recall rate of 0.571 4 and an increased F1-score of 0.658 0 in predicting drug sensitivity, exhibiting excellent generalization performance. The result demonstrates the potential of this method to assist in the selection of clinical treatment plans in the future.
近年来，精准医学在癌症治疗中已展现出广泛应用，且精准医学的重点在于准确预测不同患者对药物治疗的反应。我们提出了一种基于基因组特征分布对齐和药物结构信息来预测癌症药物敏感性的模型。该模型首先将细胞系的基因组特征与患者的基因组特征进行对齐，并去除基因表达数据中的噪声。随后，它整合药物结构特征并采用多任务学习来预测患者的药物敏感性。在癌症药物敏感性基因组学（GDSC）数据集上的实验结果表明，该方法实现了均方误差降低至0.905 2，相关系数提高至0.875 4，准确率提高至0.836 0，显著优于最近发表的方法。在癌症基因组图谱（TCGA）数据集上，该方法在预测药物敏感性时展现出平均召回率提高至0.571 4，F1分数提高至0.658 0，表现出出色的泛化性能。结果证明了该方法在未来协助临床治疗方案选择方面的潜力。
