HGSMDA: miRNA-Disease Association Prediction Based on HyperGCN and Sørensen-Dice Loss.

IF 3.6 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY Non-Coding RNA Pub Date : 2024-01-26 DOI:10.3390/ncrna10010009
Zhenghua Chang, Rong Zhu, Jinxing Liu, Junliang Shang, Lingyun Dai
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Abstract

Biological research has demonstrated the significance of identifying miRNA-disease associations in the context of disease prevention, diagnosis, and treatment. However, the utilization of experimental approaches involving biological subjects to infer these associations is both costly and inefficient. Consequently, there is a pressing need to devise novel approaches that offer enhanced accuracy and effectiveness. Presently, the predominant methods employed for predicting disease associations rely on Graph Convolutional Network (GCN) techniques. However, the Graph Convolutional Network algorithm, which is locally aggregated, solely incorporates information from the immediate neighboring nodes of a given node at each layer. Consequently, GCN cannot simultaneously aggregate information from multiple nodes. This constraint significantly impacts the predictive efficacy of the model. To tackle this problem, we propose a novel approach, based on HyperGCN and Sørensen-Dice loss (HGSMDA), for predicting associations between miRNAs and diseases. In the initial phase, we developed multiple networks to represent the similarity between miRNAs and diseases and employed GCNs to extract information from diverse perspectives. Subsequently, we draw into HyperGCN to construct a miRNA-disease heteromorphic hypergraph using hypernodes and train GCN on the graph to aggregate information. Finally, we utilized the Sørensen-Dice loss function to evaluate the degree of similarity between the predicted outcomes and the ground truth values, thereby enabling the prediction of associations between miRNAs and diseases. In order to assess the soundness of our methodology, an extensive series of experiments was conducted employing the Human MicroRNA Disease Database (HMDD v3.2) as the dataset. The experimental outcomes unequivocally indicate that HGSMDA exhibits remarkable efficacy when compared to alternative methodologies. Furthermore, the predictive capacity of HGSMDA was corroborated through a case study focused on colon cancer. These findings strongly imply that HGSMDA represents a dependable and valid framework, thereby offering a novel avenue for investigating the intricate association between miRNAs and diseases.

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HGSMDA:基于 HyperGCN 和 Sørensen-Dice Loss 的 miRNA-疾病关联预测。
生物学研究表明,确定 miRNA 与疾病的关联对于疾病的预防、诊断和治疗具有重要意义。然而,利用涉及生物实验对象的实验方法来推断这些关联既昂贵又低效。因此,迫切需要设计出能提高准确性和有效性的新型方法。目前,预测疾病关联的主要方法依赖于图卷积网络(GCN)技术。然而,图卷积网络算法是局部聚合的,每层只包含给定节点的近邻节点的信息。因此,GCN 无法同时聚合来自多个节点的信息。这一限制极大地影响了模型的预测效果。为了解决这个问题,我们提出了一种基于 HyperGCN 和 Sørensen-Dice loss(HGSMDA)的新方法,用于预测 miRNA 与疾病之间的关联。在初始阶段,我们开发了多个网络来表示 miRNA 与疾病之间的相似性,并利用 GCN 从不同角度提取信息。随后,我们借鉴 HyperGCN,利用超节点构建了 miRNA-疾病异构超图,并在图上训练 GCN 以汇总信息。最后,我们利用 Sørensen-Dice 损失函数来评估预测结果与基本真实值之间的相似程度,从而预测 miRNA 与疾病之间的关联。为了评估我们方法的合理性,我们以人类微小核糖核酸疾病数据库(HMDD v3.2)为数据集,进行了一系列广泛的实验。实验结果明确表明,与其他方法相比,HGSMDA 具有显著的功效。此外,一项以结肠癌为重点的案例研究也证实了 HGSMDA 的预测能力。这些发现有力地表明,HGSMDA 是一个可靠、有效的框架,从而为研究 miRNA 与疾病之间错综复杂的联系提供了一条新途径。
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来源期刊
Non-Coding RNA
Non-Coding RNA Biochemistry, Genetics and Molecular Biology-Genetics
CiteScore
6.70
自引率
4.70%
发文量
74
审稿时长
10 weeks
期刊介绍: Functional studies dealing with identification, structure-function relationships or biological activity of: small regulatory RNAs (miRNAs, siRNAs and piRNAs) associated with the RNA interference pathway small nuclear RNAs, small nucleolar and tRNAs derived small RNAs other types of small RNAs, such as those associated with splice junctions and transcription start sites long non-coding RNAs, including antisense RNAs, long ''intergenic'' RNAs, intronic RNAs and ''enhancer'' RNAs other classes of RNAs such as vault RNAs, scaRNAs, circular RNAs, 7SL RNAs, telomeric and centromeric RNAs regulatory functions of mRNAs and UTR-derived RNAs catalytic and allosteric (riboswitch) RNAs viral, transposon and repeat-derived RNAs bacterial regulatory RNAs, including CRISPR RNAS Analysis of RNA processing, RNA binding proteins, RNA signaling and RNA interaction pathways: DICER AGO, PIWI and PIWI-like proteins other classes of RNA binding and RNA transport proteins RNA interactions with chromatin-modifying complexes RNA interactions with DNA and other RNAs the role of RNA in the formation and function of specialized subnuclear organelles and other aspects of cell biology intercellular and intergenerational RNA signaling RNA processing structure-function relationships in RNA complexes RNA analyses, informatics, tools and technologies: transcriptomic analyses and technologies development of tools and technologies for RNA biology and therapeutics Translational studies involving long and short non-coding RNAs: identification of biomarkers development of new therapies involving microRNAs and other ncRNAs clinical studies involving microRNAs and other ncRNAs.
期刊最新文献
Cardiomyopathies: The Role of Non-Coding RNAs. MicroRNA Biogenesis, Gene Regulation Mechanisms, and Availability in Foods. Interplay of microRNAs and circRNAs in Epithelial Ovarian Cancer. Non-Coding RNA as a Biomarker in Lung Cancer. Back to the Origin: Mechanisms of circRNA-Directed Regulation of Host Genes in Human Disease.
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