School of Computer Science and Technology, Xidian University, Xi'an 710071, Shaanxi, China.
School of Computer Science and Technology, Xidian University, Xi'an 710071, Shaanxi, China.
Methods. 2021 Aug;192:85-92. doi: 10.1016/j.ymeth.2020.08.006. Epub 2020 Aug 13.
Predicting the response of each individual patient to a drug is a key issue assailing personalized medicine. Our study predicted drug response based on the fusion of multiomics data with low-dimensional feature vector representation on a multilayer network model. We named this new method DREMO (Drug Response prEdiction based on MultiOmics data fusion). DREMO fuses similarities between cell lines and similarities between drugs, thereby improving the ability to predict the response of cancer cell lines to therapeutic agents. First, a multilayer similarity network related to cell lines and drugs was constructed based on gene expression profiles, somatic mutation, copy number variation (CNV), drug chemical structures, and drug targets. Next, low-dimensional feature vector representation was used to fuse the biological information in the multilayer network. Then, a machine learning model was applied to predict new drug-cell line associations. Finally, our results were validated using the well-established GDSC/CCLE databases, literature, and the functional pathway database. Furthermore, a comparison was made between DREMO and other methods. Results of the comparison showed that DREMO improves predictive capabilities significantly.
预测每个患者对药物的反应是个性化医疗面临的关键问题。我们的研究基于多层网络模型，通过融合多组学数据和低维特征向量表示来预测药物反应。我们将这种新方法命名为 DREMO（基于多组学数据融合的药物反应预测）。DREMO 融合了细胞系之间的相似性和药物之间的相似性，从而提高了预测癌细胞系对治疗剂反应的能力。首先，基于基因表达谱、体细胞突变、拷贝数变异（CNV）、药物化学结构和药物靶点构建了一个与细胞系和药物相关的多层相似性网络。接下来，使用低维特征向量表示来融合多层网络中的生物学信息。然后，应用机器学习模型来预测新的药物-细胞系关联。最后，使用成熟的 GDSC/CCLE 数据库、文献和功能途径数据库对我们的结果进行了验证。此外，还对 DREMO 与其他方法进行了比较。比较结果表明，DREMO 显著提高了预测能力。
