MCMVDRP:用于癌症药物反应预测的多通道多视角深度学习框架。

IF 1.5 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Xiangyu Li, Xiumin Shi, Yuxuan Li, Lu Wang
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引用次数: 0

摘要

药物治疗仍然是治疗肿瘤的主要方法。癌症患者之间的差异,包括基因组特征的差异,往往导致同一批癌症患者对类似的抗癌药物治疗产生不同的治疗反应。因此,通过分析个体患者的基因组特征来预测药物反应具有重要的研究意义。随着机器学习和深度学习的显著进步,出现了许多利用药物和细胞系特征预测药物反应的有效方法。然而,这些方法不足以捕捉到足够数量的药物固有特征。因此,我们提出了一种药物表征方法,其中包含三种不同类型的特征:分子图、SMILE 字符串和分子指纹。在这项研究中,我们引入了一种名为 MCMVDRP 的新型深度学习模型,用于预测癌症药物反应。在我们提出的模型中,首先对这些提取的特征进行合并,然后利用全连接层根据 IC50 值预测药物反应。实验结果表明,所提出的模型在性能上优于目前最先进的模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
MCMVDRP: a multi-channel multi-view deep learning framework for cancer drug response prediction.

Drug therapy remains the primary approach to treating tumours. Variability among cancer patients, including variations in genomic profiles, often results in divergent therapeutic responses to analogous anti-cancer drug treatments within the same cohort of cancer patients. Hence, predicting the drug response by analysing the genomic profile characteristics of individual patients holds significant research importance. With the notable progress in machine learning and deep learning, many effective methods have emerged for predicting drug responses utilizing features from both drugs and cell lines. However, these methods are inadequate in capturing a sufficient number of features inherent to drugs. Consequently, we propose a representational approach for drugs that incorporates three distinct types of features: the molecular graph, the SMILE strings, and the molecular fingerprints. In this study, a novel deep learning model, named MCMVDRP, is introduced for the prediction of cancer drug responses. In our proposed model, an amalgamation of these extracted features is performed, followed by the utilization of fully connected layers to predict the drug response based on the IC50 values. Experimental results demonstrate that the presented model outperforms current state-of-the-art models in performance.

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来源期刊
Journal of Integrative Bioinformatics
Journal of Integrative Bioinformatics Medicine-Medicine (all)
CiteScore
3.10
自引率
5.30%
发文量
27
审稿时长
12 weeks
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