HiG2Vec: hierarchical representations of Gene Ontology and genes in the Poincaré ball.
HiG2Vec: hierarchical representations of Gene Ontology and genes in the Poincaré ball.
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DOI：
10.1093/bioinformatics/btab193
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发表时间：
2021-09-29
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基因本体（GO）和基因本体标注（GOA）的知识操作主要通过对基因本体术语和基因的向量表示来实现。以前的研究使用基于word2vec的嵌入方法将实体表示为数字向量，将GO术语和基因或基因产物表示为欧几里得空间，以测量它们的语义相似度。然而，该方法的局限性在于，在欧几里得空间中嵌入大型图结构数据不能防止潜在层次信息的丢失，从而无法最优地捕获GO和GOA的语义。另一方面，双曲空间，如庞加莱球，更适合于建模层次结构，因为它们具有几何性质，当它接近边界时，由于负曲率，距离呈指数增长。在本文中，我们提出了GO和基因（HiG2Vec）的分层表示，通过两步程序：GO嵌入和基因嵌入，应用专门用于层次表示的poincar<e:1>嵌入。通过实验，我们表明我们的模型比其他方法更好地代表了层次结构，并且预测基因或基因产物的相互作用与之前的研究相似或更好。结果表明，HiG2Vec在GO和基因语义的捕获以及数据利用方面都优于其他方法。它可以稳健地应用于操纵各种生物知识。https://github.com/JaesikKim/HiG2Vec。补充数据可在生物信息学网站获得。
Knowledge manipulation of Gene Ontology (GO) and Gene Ontology Annotation (GOA) can be done primarily by using vector representation of GO terms and genes. Previous studies have represented GO terms and genes or gene products in Euclidean space to measure their semantic similarity using an embedding method such as the Word2Vec-based method to represent entities as numeric vectors. However, this method has the limitation that embedding large graph-structured data in the Euclidean space cannot prevent a loss of information of latent hierarchies, thus precluding the semantics of GO and GOA from being captured optimally. On the other hand, hyperbolic spaces such as the Poincaré balls are more suitable for modeling hierarchies, as they have a geometric property in which the distance increases exponentially as it nears the boundary because of negative curvature. In this article, we propose hierarchical representations of GO and genes (HiG2Vec) by applying Poincaré embedding specialized in the representation of hierarchy through a two-step procedure: GO embedding and gene embedding. Through experiments, we show that our model represents the hierarchical structure better than other approaches and predicts the interaction of genes or gene products similar to or better than previous studies. The results indicate that HiG2Vec is superior to other methods in capturing the GO and gene semantics and in data utilization as well. It can be robustly applied to manipulate various biological knowledge. https://github.com/JaesikKim/HiG2Vec. Supplementary data are available at Bioinformatics online.
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影响因子：
3
作者：
Jain S;Bader GD
通讯作者：
Bader GD
影响因子：
3
作者：
Guo FB;Zhang CT
通讯作者：
Zhang CT
影响因子：
14.9
作者：
Clark K;Karsch-Mizrachi I;Lipman DJ;Ostell J;Sayers EW
通讯作者：
Sayers EW
影响因子：
6.8
作者：
Bonnabel, Silvere
通讯作者：
Bonnabel, Silvere
DOI：
10.1093/bioinformatics/bty559
发表时间：
2018-09-01
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Alshahrani M;Hoehndorf R
通讯作者：
Hoehndorf R