Graph embeddings on gene ontology annotations for protein-protein interaction prediction

Protein-protein interaction (PPI) prediction is an important task towards the understanding of many bioinformatics functions and applications, such as predicting protein functions, gene-disease associations and disease-drug associations. However, many previous PPI prediction researches do not consid...

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Main Authors: Zhong, Xiaoshi, Rajapakse, Jagath Chandana
Other Authors: School of Computer Science and Engineering
Format: Article
Language:English
Published: 2021
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Online Access:https://hdl.handle.net/10356/146133
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1461332021-01-27T06:19:21Z Graph embeddings on gene ontology annotations for protein-protein interaction prediction Zhong, Xiaoshi Rajapakse, Jagath Chandana School of Computer Science and Engineering Engineering::Computer science and engineering Graph Embeddings Vector Representations Protein-protein interaction (PPI) prediction is an important task towards the understanding of many bioinformatics functions and applications, such as predicting protein functions, gene-disease associations and disease-drug associations. However, many previous PPI prediction researches do not consider missing and spurious interactions inherent in PPI networks. To address these two issues, we define two corresponding tasks, namely missing PPI prediction and spurious PPI prediction, and propose a method that employs graph embeddings that learn vector representations from constructed Gene Ontology Annotation (GOA) graphs and then use embedded vectors to achieve the two tasks. Our method leverages on information from both term-term relations among GO terms and term-protein annotations between GO terms and proteins, and preserves properties of both local and global structural information of the GO annotation graph. Ministry of Education (MOE) Published version Publication of this article was funded by the Tier-2 Grant MOE2016-T2-1-029 and the Tier-1 Grant MOE2019-T1-002-057 from the Ministry of Education, Singapore. The funding bodies had no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript. 2021-01-27T06:19:20Z 2021-01-27T06:19:20Z 2020 Journal Article Zhong, X., & Rajapakse, J. C. (2020). Graph embeddings on gene ontology annotations for protein–protein interaction prediction. BMC Bioinformatics, 21(S16), 560-. doi:10.1186/s12859-020-03816-8 1471-2105 0000-0002-6108-272X https://hdl.handle.net/10356/146133 10.1186/s12859-020-03816-8 33323115 2-s2.0-85097563903 Suppl 16 21 en MOE2016-T2-1-029 MOE2019-T1-002-057 BMC bioinformatics © 2020 The Author(s). This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Computer science and engineering
Graph Embeddings
Vector Representations
spellingShingle Engineering::Computer science and engineering
Graph Embeddings
Vector Representations
Zhong, Xiaoshi
Rajapakse, Jagath Chandana
Graph embeddings on gene ontology annotations for protein-protein interaction prediction
description Protein-protein interaction (PPI) prediction is an important task towards the understanding of many bioinformatics functions and applications, such as predicting protein functions, gene-disease associations and disease-drug associations. However, many previous PPI prediction researches do not consider missing and spurious interactions inherent in PPI networks. To address these two issues, we define two corresponding tasks, namely missing PPI prediction and spurious PPI prediction, and propose a method that employs graph embeddings that learn vector representations from constructed Gene Ontology Annotation (GOA) graphs and then use embedded vectors to achieve the two tasks. Our method leverages on information from both term-term relations among GO terms and term-protein annotations between GO terms and proteins, and preserves properties of both local and global structural information of the GO annotation graph.
author2 School of Computer Science and Engineering
author_facet School of Computer Science and Engineering
Zhong, Xiaoshi
Rajapakse, Jagath Chandana
format Article
author Zhong, Xiaoshi
Rajapakse, Jagath Chandana
author_sort Zhong, Xiaoshi
title Graph embeddings on gene ontology annotations for protein-protein interaction prediction
title_short Graph embeddings on gene ontology annotations for protein-protein interaction prediction
title_full Graph embeddings on gene ontology annotations for protein-protein interaction prediction
title_fullStr Graph embeddings on gene ontology annotations for protein-protein interaction prediction
title_full_unstemmed Graph embeddings on gene ontology annotations for protein-protein interaction prediction
title_sort graph embeddings on gene ontology annotations for protein-protein interaction prediction
publishDate 2021
url https://hdl.handle.net/10356/146133
_version_ 1690658491961704448