School of Computer Software, College of Intelligence and Computing, Tianjin University, Tianjin, China.
School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, China.
Methods. 2019 Aug 15;166:91-102. doi: 10.1016/j.ymeth.2019.02.009. Epub 2019 Feb 14.
The identification of therapeutic biomarkers predictive of drug response is crucial in personalized medicine. A number of computational models to predict response of anti-cancer drugs have been developed as the establishment of several pharmacogenomics screening databases. In our study, we proposed a deep cascaded forest model, Deep-Resp-Forest, to classify the anti-cancer drug response as "sensitive" or "resistant". We made three contributions in this study. Firstly, diverse molecular data could be effectively integrated to provide more information than single type of data for the classification. Combination of two types of data were tested here. Secondly, two structures based on the multi-grained scanning to transform the raw features into high-dimensional feature vectors and integrate the diverse data were proposed in our study. Thirdly, the original deep and time-consuming architecture of cascade forest was improved by a feature optimization operation, which emphasized the most discriminative features across layers. We evaluated the proposed method on the Cancer Cell Line Encyclopedia (CCLE) and Genomics of Drug Sensitivity in Cancer (GDSC) data sets and then compared with the Support Vector Machine. The proposed Deep-Resp-Forest has demonstrated the promising use of deep learning and deep forest approach on the drug response prediction tasks. The R implementation for running our experiments is available athttps://github.com/RanSuLab/Deep-Resp-Forest.
预测药物反应的治疗生物标志物的鉴定在个性化医疗中至关重要。已经开发了许多计算模型来预测抗癌药物的反应，作为建立几个药物基因组学筛选数据库的一部分。在我们的研究中，我们提出了一个深度级联森林模型 Deep-Resp-Forest，用于将抗癌药物反应分类为“敏感”或“耐药”。我们在这项研究中有三个贡献。首先，可以有效地整合多种分子数据，为分类提供比单一类型数据更多的信息。这里测试了两种类型数据的组合。其次，我们提出了两种基于多粒度扫描的结构，将原始特征转换为高维特征向量，并整合不同的数据。第三，通过特征优化操作改进了原始的深度和耗时的级联森林架构，该操作强调了跨层最具区分性的特征。我们在癌症细胞系百科全书（CCLE）和癌症药物敏感性基因组学（GDSC）数据集上评估了所提出的方法，然后与支持向量机进行了比较。所提出的 Deep-Resp-Forest 表明深度学习和深度森林方法在药物反应预测任务中的应用具有广阔的前景。用于运行我们的实验的 R 实现可在 https://github.com/RanSuLab/Deep-Resp-Forest 上获得。
