Word and Sentence Embedding Tools to Measure Semantic Similarity of Gene Ontology Terms by Their Definitions.

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 article, 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

SUBMITTER: Duong D 

PROVIDER: S-EPMC6350067 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature