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Confirmatory Results

Word and sentence embedding tools to measure semantic similarity of Gene Ontology terms by their definitions

Dat Duong, Wasi Uddin Ahmad, Eleazar Eskin, Kai-Wei Chang, Jingyi Jessica Li
doi: https://doi.org/10.1101/103648
Dat Duong
1Department of Computer Science, University of California, Los Angeles, United States of America.
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Wasi Uddin Ahmad
1Department of Computer Science, University of California, Los Angeles, United States of America.
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Eleazar Eskin
1Department of Computer Science, University of California, Los Angeles, United States of America.
3Department of Human Genetics, University of California, Los Angeles, United States of America.
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Kai-Wei Chang
1Department of Computer Science, University of California, Los Angeles, United States of America.
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Jingyi Jessica Li
2Department of Statistics, University of California, Los Angeles, United States of America.
3Department of Human Genetics, University of California, Los Angeles, United States of America.
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Abstract

The Gene Ontology (GO) database contains GO terms that describe biological functions of genes. Previous methods for comparing GO terms have relied on the fact that GO terms are organized into a tree structure. Under this paradigm, the locations of two GO terms in the tree dictate their similarity score. In this paper, we introduce two new solutions for this problem, by focusing instead on the definitions of the GO terms. We apply neural network based techniques from the natural language processing (NLP) domain. The first method does not rely on the GO tree, whereas the second indirectly depends on the GO tree. In our first approach, we compare two GO definitions by treating them as two unordered sets of words. The word similarity is estimated by a word embedding model that maps words into an N-dimensional space. In our second approach, we account for the word-ordering within a sentence. We use a sentence encoder to embed GO definitions into vectors and estimate how likely one definition entails another. We validate our methods in two ways. In the first experiment, we test the model’s ability to differentiate a true protein-protein network from a randomly generated network. In the second experiment, we test the model in identifying orthologs from randomly-matched genes in human, mouse, and fly. In both experiments, a hybrid of NLP and GO-tree based method achieves the best classification accuracy.

Footnotes

  • Availability: github.com/datduong/NLPMethods2CompareGOterms

  • ↵* Contact: datdb{at}cs.ucla.edu, jli{at}stat.ucla.edu

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted May 14, 2018.
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Word and sentence embedding tools to measure semantic similarity of Gene Ontology terms by their definitions
Dat Duong, Wasi Uddin Ahmad, Eleazar Eskin, Kai-Wei Chang, Jingyi Jessica Li
bioRxiv 103648; doi: https://doi.org/10.1101/103648
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Word and sentence embedding tools to measure semantic similarity of Gene Ontology terms by their definitions
Dat Duong, Wasi Uddin Ahmad, Eleazar Eskin, Kai-Wei Chang, Jingyi Jessica Li
bioRxiv 103648; doi: https://doi.org/10.1101/103648

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