Machine Learning Models for Automatic Gene Ontology Annotation of Biological Texts

Abstract

Gene ontology (GO) is a major source of biological knowledge that describes the functions of genes and gene products using a comprehensive set of controlled vocabularies or terms organized in a hierarchical structure. Automatic annotation of biological texts using gene ontology (GO) terms gained the attention of the scientific community as it helps to quickly identify relevant documents or parts of text related to specific biological functions or processes. In this paper, we propose and investigate a new GO-term annotation strategy that uses a non-parametric k-nearest neighbor model and relies on various vector-based representations of documents and GO terms linked to these documents. Our vector representations are based on machine learning and natural language processing (NLP) models, including singular value decomposition, Word2Vec and topic-based scoring. We evaluate the performance of our model on a large benchmark corpus using a variety of standard and hierarchical evaluation metrics.

Supported by the Defense Advanced Research Projects Agency (DARPA) through Cooperative Agreement D20AC00002 awarded by the U.S. Department of the Interior, Interior Business Center. The content of the article does not necessarily reflect the position or the policy of the Government, and no official endorsement should be inferred.