HiG2Vec: hierarchical representations of Gene Ontology and genes in the Poincaré ball

Author:

Kim Jaesik123,Kim Dokyoon23ORCID,Sohn Kyung-Ah14ORCID

Affiliation:

1. Department of Computer Engineering, Ajou University, Suwon 16499, South Korea

2. Department of Biostatistics, Epidemiology & Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA

3. Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA

4. Department of Artificial Intelligence, Ajou University, Suwon 16499, South Korea

Abstract

Abstract Motivation 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. Results 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. Availabilityand implementation https://github.com/JaesikKim/HiG2Vec. Supplementary information Supplementary data are available at Bioinformatics online.

Funder

National Research Foundation of Korea

Korea government

ITRC

IITP

National Institutes of Health

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

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