SAGCN: Using graph convolutional network with subgraph-aware for circRNA-drug sensitivity identification.

IF 3.6 3区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS IEEE/ACM Transactions on Computational Biology and Bioinformatics Pub Date : 2024-06-17 DOI:10.1109/TCBB.2024.3415058
Weicheng Sun, Chengjuan Ren, Jinsheng Xu, Ping Zhang
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Abstract

Circular RNAs (circRNAs) play a significant role in cancer development and therapy resistance. There is substantial evidence indicating that the expression of circRNAs affects the sensitivity of cells to drugs. Identifying circRNAs-drug sensitivity association (CDA) is helpful for disease treatment and drug discovery. However, the identification of CDA through conventional biological experiments is both time-consuming and costly. Therefore, it is urgent to develop computational methods to predict CDA. In this study, we propose a new computational method, the subgraph-aware graph convolutional network (SAGCN), for predicting CDA. SAGCN first construct a heterogeneous network composed of circRNA similarity network, drug similarity network, and circRNA-drug bipartite network. Then, a subgraph extractor is proposed to learn the latent subgraph structure of the heterogeneous network using a graph convolutional network. The extractor can capture 1-hop and 2-hop information and then a fusing attention mechanism is designed to integrate them adaptively. Simultaneously, a novel subgraph-aware attention mechanism is proposed to detect intrinsic subgraph structure. The final node feature representation is obtained to make the CDA prediction. Experimental results demonstrate that SAGCN obtained an average AUC of 0.9120 and AUPR of 0.8693, exceeding the performance of the most advanced models under 10-fold cross-validation. Case studies have demonstrated the potential of SAGCN in identifying associations between circRNA and drug sensitivity.

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SAGCN:使用具有子图感知功能的图卷积网络进行 circRNA 药物敏感性识别。
环状 RNA(circRNA)在癌症发展和抗药性方面发挥着重要作用。大量证据表明,circRNAs 的表达会影响细胞对药物的敏感性。鉴定 circRNAs-药物敏感性关联(CDA)有助于疾病治疗和药物发现。然而,通过传统生物学实验鉴定 CDA 既费时又费钱。因此,开发预测 CDA 的计算方法迫在眉睫。在本研究中,我们提出了一种预测 CDA 的新计算方法--子图感知图卷积网络(SAGCN)。SAGCN 首先构建一个由 circRNA 相似性网络、药物相似性网络和 circRNA-药物二元网络组成的异构网络。然后,提出一种子图提取器,利用图卷积网络学习异构网络的潜在子图结构。该提取器可以捕捉 1 跳和 2 跳信息,然后设计了一种融合关注机制来自适应地整合这些信息。同时,还提出了一种新颖的子图感知关注机制来检测内在的子图结构。最终得到的节点特征表示可用于 CDA 预测。实验结果表明,SAGCN 的平均 AUC 为 0.9120,AUPR 为 0.8693,超过了 10 倍交叉验证下最先进模型的性能。案例研究证明了 SAGCN 在识别 circRNA 与药物敏感性之间关联方面的潜力。
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来源期刊
CiteScore
7.50
自引率
6.70%
发文量
479
审稿时长
3 months
期刊介绍: IEEE/ACM Transactions on Computational Biology and Bioinformatics emphasizes the algorithmic, mathematical, statistical and computational methods that are central in bioinformatics and computational biology; the development and testing of effective computer programs in bioinformatics; the development of biological databases; and important biological results that are obtained from the use of these methods, programs and databases; the emerging field of Systems Biology, where many forms of data are used to create a computer-based model of a complex biological system
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