ArticleJournal of computational biology : a journal of computational molecular cell biology2019
Word and Sentence Embedding Tools to Measure Semantic Similarity of Gene Ontology Terms by Their Definitions.
Article in Journal of computational biology : a journal of computational molecular cell biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- iEnhancer-GDM: A Deep Learning Framework Based on Generative Adversarial Network and Multi-head Attention Mechanism to Identify Enhancers and Their Strength.Interdisciplinary sciences, computational life sciences · 2025Article
- Pathway Analysis Interpretation in the Multi-Omic Era.Biotech (Basel (Switzerland)) · 2025Review
- Simple and effective embedding model for single-cell biology built from ChatGPT.Nature biomedical engineering · 2025Article
- GenePT: A Simple But Effective Foundation Model for Genes and Cells Built From ChatGPT.bioRxiv : the preprint server for biology · 2024Article
- Interpreting and visualizing pathway analyses using embedding representations with PAVER.Bioinformation · 2024Article
- Using language models and ontology topology to perform semantic mapping of traits between biomedical datasets.Bioinformatics (Oxford, England) · 2023Article
- The positive energy of netizens: development and application of fine-grained sentiment lexicon and emotional intensity model.Current psychology (New Brunswick, N.J.) · 2022Article
- InfersentPPI: Prediction of Protein-Protein Interaction Using Protein Sentence Embedding With Gene Ontology Information.Frontiers in genetics · 2022Article
- DNA sequences performs as natural language processing by exploiting deep learning algorithm for the identification of N4-methylcytosine.Scientific reports · 2021Article
- Graph embeddings on gene ontology annotations for protein-protein interaction prediction.BMC bioinformatics · 2020Article
- GO2Vec: transforming GO terms and proteins to vector representations via graph embeddings.BMC genomics · 2019Article
- Identifying antimicrobial peptides using word embedding with deep recurrent neural networks.Bioinformatics (Oxford, England) · 2019Article
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5 authors.
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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 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 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.
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