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首页> 外文会议>International Computer Conference, Computer Society of Iran >A New Disease Candidate Gene Prioritization Method Using Graph Convolutional Networks
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A New Disease Candidate Gene Prioritization Method Using Graph Convolutional Networks

机译：一种新的疾病候选基因优先化方法，使用图卷积网络

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摘要

Identifying disease genes from a large number of candidate genes by laboratory methods is very costly and time consuming, so it is necessary to prioritize disease candidate genes before laboratory work. Recently, many gene prioritization methods have been proposed using various datasets such as gene ontology and protein-protein interaction, which are often based on text mining, machine learning, and random walk methods. Due to the good performance and increasing use of deep graph networks in the representation of graph problems, in this study, a method based on graph convolutional networks has been developed to represent the graph on the protein-protein interaction. The results show that the proposed method is effective and the performance of the proposed method better than other methods in some cases.
机译：通过实验方法鉴定来自大量候选基因的疾病基因非常昂贵且耗时，因此在实验室工作之前有必要优先考虑疾病候选基因。 最近，已经使用各种数据集如基因本体和蛋白质 - 蛋白质相互作用提出了许多基因优先化方法，这些方法通常基于文本挖掘，机器学习和随机步行方法。 由于良好的性能和越来越多地利用深图网络在图中的表现中，在本研究中，已经开发了一种基于图形卷积网络的方法来表示蛋白质 - 蛋白质相互作用的图表。 结果表明，该方法在某些情况下比其他方法更具有效性，性能优于其他方法。

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