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Random Walks on Biomedical Networks
Current Proteomics ( IF 0.8 ) Pub Date : 2021-09-30 , DOI: 10.2174/1570164617999200731001544
Guiyang Zhang 1 , Pan Wang 1 , You Li 1 , Guohua Huang 1
Affiliation  

The biomedical network is becoming a fundamental tool to represent sophisticated biosystems, while Random Walk (RW) models on it are becoming a sharp sword to address such challenging issues as gene function annotation, drug target identification, and disease biomarker recognition. Recently, numerous random walk models have been proposed and applied to biomedical networks. Due to good performances, the random walk is attracting increasing attentions from multiple communities. In this survey, we firstly introduced various random walk models, with emphasis on the PageRank and the random walk with restart. We then summarized applications of the random work RW on the biomedical networks from the graph learning point of view, which mainly included node classification, link prediction, cluster/community detection, and learning representation of the node. We discussed briefly its limitation and existing issues also.



中文翻译:

生物医学网络上的随机游走

生物医学网络正在成为表示复杂生物系统的基本工具,而其上的随机游走 (RW) 模型正在成为解决诸如基因功能注释、药物靶点识别和疾病生物标志物识别等具有挑战性问题的利剑。最近,已经提出了许多随机游走模型并应用于生物医学网络。由于表现良好,随机游走越来越受到多个社区的关注。在本次调查中,我们首先介绍了各种随机游走模型,重点介绍了 PageRank 和重新启动的随机游走。然后我们从图学习的角度总结了随机工作RW在生物医学网络上的应用,主要包括节点分类、链接预测、集群/社区检测和节点的学习表示。

更新日期:2021-11-23
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