Hierarchical graph representation learning with multi-granularity features for anti-cancer drug response prediction.

IF 6.7 2区 医学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS IEEE Journal of Biomedical and Health Informatics Pub Date : 2024-11-06 DOI:10.1109/JBHI.2024.3492806
Wei Peng, Jiangzhen Lin, Wei Dai, Ning Yu, Jianxin Wang
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Abstract

Patients with the same type of cancer often respond differently to identical drug treatments due to unique genomic traits. Accurately predicting a patient's response to drug is crucial in guiding treatment decisions, alleviating patient suffering, and improving cancer prognosis. Current computational methods utilize deep learning models trained on extensive drug screening data to predict anti-cancer drug responses based on features of cell lines and drugs. However, the interaction between cell lines and drugs is a complex biological process involving interactions across various levels, from internal cellular and drug structures to the external interactions among different molecules.To address this complexity, we propose a novel Hierarchical graph representation Learning with Multi-Granularity features (HLMG) algorithm for predicting anti-cancer drug responses. The HLMG algorithm combines features at two granularities: the overall gene expression and pathway substructures of cell lines, and the overall molecular fingerprints and substructures of drugs. Subsequently, it constructs a heterogeneous graph including cell lines, drugs, known cell line-drug responses, and the associations between similar cell lines and similar drugs. Through a graph convolutional network model, the HLMG learns the final cell line and drug representations by aggregating features of their multi-level neighbor in the heterogeneous graph. The multi-level neighbors consist of the node self, directly related drugs/cell lines, and indirectly related similar drugs/cell lines. Finally, a linear correlation coefficient decoder is employed to reconstruct the cell line-drug correlation matrix to predict anti-cancer drug responses. Our model was tested on the Genomics of Drug Sensitivity in Cancer (GDSC) and the Cancer Cell Line Encyclopedia (CCLE) databases. Results indicate that HLMG outperforms other state-of-the-art methods in accurately predicting anti-cancer drug responses.

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利用多粒度特征进行分层图表示学习以预测抗癌药物反应
由于独特的基因组特征,同种癌症患者对相同药物治疗的反应往往不同。准确预测患者对药物的反应对于指导治疗决策、减轻患者痛苦和改善癌症预后至关重要。目前的计算方法利用在大量药物筛选数据基础上训练的深度学习模型,根据细胞系和药物的特征预测抗癌药物反应。然而,细胞系与药物之间的相互作用是一个复杂的生物学过程,涉及从细胞和药物内部结构到不同分子之间外部相互作用等各个层面。HLMG 算法结合了两种粒度的特征:细胞系的整体基因表达和通路子结构,以及药物的整体分子指纹和子结构。随后,它构建了一个异构图,其中包括细胞系、药物、已知的细胞系-药物反应,以及相似细胞系和相似药物之间的关联。通过图卷积网络模型,HLMG 通过聚合异构图中多层次邻居的特征来学习最终的细胞系和药物表征。多级邻居包括节点自身、直接相关的药物/细胞系和间接相关的类似药物/细胞系。最后,采用线性相关系数解码器重建细胞系-药物相关矩阵,预测抗癌药物反应。我们的模型在癌症药物敏感性基因组学(GDSC)和癌症细胞系百科全书(CCLE)数据库上进行了测试。结果表明,在准确预测抗癌药物反应方面,HLMG优于其他最先进的方法。
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来源期刊
IEEE Journal of Biomedical and Health Informatics
IEEE Journal of Biomedical and Health Informatics COMPUTER SCIENCE, INFORMATION SYSTEMS-COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
CiteScore
13.60
自引率
6.50%
发文量
1151
期刊介绍: IEEE Journal of Biomedical and Health Informatics publishes original papers presenting recent advances where information and communication technologies intersect with health, healthcare, life sciences, and biomedicine. Topics include acquisition, transmission, storage, retrieval, management, and analysis of biomedical and health information. The journal covers applications of information technologies in healthcare, patient monitoring, preventive care, early disease diagnosis, therapy discovery, and personalized treatment protocols. It explores electronic medical and health records, clinical information systems, decision support systems, medical and biological imaging informatics, wearable systems, body area/sensor networks, and more. Integration-related topics like interoperability, evidence-based medicine, and secure patient data are also addressed.
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