Onto2Vec: joint vector-based representation of biological entities and their ontology-based annotations
Type
ArticleKAUST Department
Bio-Ontology Research Group (BORG)Computational Bioscience Research Center (CBRC)
Computer Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Structural and Functional Bioinformatics Group
KAUST Grant Number
URF/1/1976-04URF/1/1976-06
URF/1/3450-01-01
URF/1/3454-01-01
Date
2018-06-27Preprint Posting Date
2018-01-31Online Publication Date
2018-06-27Print Publication Date
2018-07-01Permanent link to this record
http://hdl.handle.net/10754/627122
Metadata
Show full item recordAbstract
Motivation Biological knowledge is widely represented in the form of ontology-based annotations: ontologies describe the phenomena assumed to exist within a domain, and the annotations associate a (kind of) biological entity with a set of phenomena within the domain. The structure and information contained in ontologies and their annotations make them valuable for developing machine learning, data analysis and knowledge extraction algorithms; notably, semantic similarity is widely used to identify relations between biological entities, and ontology-based annotations are frequently used as features in machine learning applications. Results We propose the Onto2Vec method, an approach to learn feature vectors for biological entities based on their annotations to biomedical ontologies. Our method can be applied to a wide range of bioinformatics research problems such as similarity-based prediction of interactions between proteins, classification of interaction types using supervised learning, or clustering. To evaluate Onto2Vec, we use the gene ontology (GO) and jointly produce dense vector representations of proteins, the GO classes to which they are annotated, and the axioms in GO that constrain these classes. First, we demonstrate that Onto2Vec-generated feature vectors can significantly improve prediction of protein–protein interactions in human and yeast. We then illustrate how Onto2Vec representations provide the means for constructing data-driven, trainable semantic similarity measures that can be used to identify particular relations between proteins. Finally, we use an unsupervised clustering approach to identify protein families based on their Enzyme Commission numbers. Our results demonstrate that Onto2Vec can generate high quality feature vectors from biological entities and ontologies. Onto2Vec has the potential to significantly outperform the state-of-the-art in several predictive applications in which ontologies are involved.Citation
Smaili, F. Z., Gao, X., & Hoehndorf, R. (2018). Onto2Vec: joint vector-based representation of biological entities and their ontology-based annotations. Bioinformatics, 34(13), i52–i60. doi:10.1093/bioinformatics/bty259Publisher
Oxford University Press (OUP)Journal
BioinformaticsarXiv
1802.00864Additional Links
http://arxiv.org/abs/1802.00864v1http://arxiv.org/pdf/1802.00864v1
Relations
Is Supplemented By:- [Software]
Title: bio-ontology-research-group/onto2vec: Representation learning for ontologies and their annotations. Publication Date: 2018-01-07. github: bio-ontology-research-group/onto2vec Handle: 10754/667033
ae974a485f413a2113503eed53cd6c53
10.1093/bioinformatics/bty259