Significant Subgraph Detection in Multi-omics Networks for Disease Pathway Identification.
Significant Subgraph Detection in Multi-omics Networks for Disease Pathway Identification.
复制标题
多摩斯网络中的显着子图检测用于疾病途径鉴定。
DOI：
10.3389/fdata.2022.894632
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发表时间：
2022
影响因子：
3.1
通讯作者：
Banaei-Kashani F
中科院分区：
文献类型：
--
作者：
Abdel-Hafiz M;Najafi M;Helmi S;Pratte KA;Zhuang Y;Liu W;Kechris KJ;Bowler RP;Lange L;Banaei-Kashani F
慢性阻塞性肺病（COPD）是美国的主要死因之一。COPD代表了许多研究领域之一，其中识别相互作用的生物标志物的复杂途径和网络是研究疾病进展和潜在发现治疗的重要途径。最近，开发了稀疏多典型相关网络分析（SmCCNet）来识别与疾病表型（如肺功能）相关的组学之间的复杂关系。SmCCNet使用两组学数据集和相关的输出表型来生成多组学图，然后可以用于探索疾病背景下组学之间的关系。检测该多组学网络内的重要子图，即，表现出与疾病表型的高度相关性和高度互连性的子图可以帮助临床医生识别疾病进展中涉及的复杂生物学关系。目前识别重要子图的方法依赖于层次聚类，它可以用于告知临床医生有关疾病或感兴趣表型中涉及的重要途径。对层次聚类方法的依赖可能会通过偏向于寻找更紧凑的子图和删除更大的重要子图来阻碍子图质量。本研究旨在介绍新的重要子图检测技术。特别是，我们引入两个子图检测方法，称为相关PageRank和相关Louvain，通过扩展个性化PageRank聚类和Louvain算法，以及一个混合的方法结合这两种方法，并将它们与目前使用的分层方法进行比较。所提出的方法显示出显着的改进时，所产生的子图的质量相比，目前的最先进的。
Chronic obstructive pulmonary disease (COPD) is one of the leading causes of death in the United States. COPD represents one of many areas of research where identifying complex pathways and networks of interacting biomarkers is an important avenue toward studying disease progression and potentially discovering cures. Recently, sparse multiple canonical correlation network analysis (SmCCNet) was developed to identify complex relationships between omics associated with a disease phenotype, such as lung function. SmCCNet uses two sets of omics datasets and an associated output phenotypes to generate a multi-omics graph, which can then be used to explore relationships between omics in the context of a disease. Detecting significant subgraphs within this multi-omics network, i.e., subgraphs which exhibit high correlation to a disease phenotype and high inter-connectivity, can help clinicians identify complex biological relationships involved in disease progression. The current approach to identifying significant subgraphs relies on hierarchical clustering, which can be used to inform clinicians about important pathways involved in the disease or phenotype of interest. The reliance on a hierarchical clustering approach can hinder subgraph quality by biasing toward finding more compact subgraphs and removing larger significant subgraphs. This study aims to introduce new significant subgraph detection techniques. In particular, we introduce two subgraph detection methods, dubbed Correlated PageRank and Correlated Louvain, by extending the Personalized PageRank Clustering and Louvain algorithms, as well as a hybrid approach combining the two proposed methods, and compare them to the hierarchical method currently in use. The proposed methods show significant improvement in the quality of the subgraphs produced when compared to the current state of the art.
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影响因子：
3
作者：
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通讯作者：
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影响因子：
5.7
作者：
Alaimo S;Giugno R;Pulvirenti A
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影响因子：
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作者：
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Pardalos, Panos M.
影响因子：
1.9
作者：
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通讯作者：
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DOI：
10.1073/pnas.0605965104
发表时间：
2007-01-02
影响因子：
11.1
作者：
Fortunato, Santo;Barthelemy, Marc
通讯作者：
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