Assessment of Semantic Similarity between Proteins Using Information Content and Topological Properties of the Gene Ontology Graph

2018 ◽  
Vol 15 (3) ◽  
pp. 839-849 ◽  
Author(s):  
Pritha Dutta ◽  
Subhadip Basu ◽  
Mahantapas Kundu
Keyword(s):  
Gene Ontology ◽  
Information Content ◽  
Semantic Similarity ◽  
Topological Properties

scholarly journals Information Content-Based Gene Ontology Semantic Similarity Approaches: Toward a Unified Framework Theory

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Gaston K. Mazandu ◽  
Nicola J. Mulder
Keyword(s):  
Gene Ontology ◽  
Information Content ◽  
Semantic Similarity ◽  
Experimental Evaluation ◽  
Similarity Measures ◽  
Mathematical Framework ◽  
Unified Framework ◽  
The Impact ◽  
Unified Description ◽  
Similarity Scores

Several approaches have been proposed for computing term information content (IC) and semantic similarity scores within the gene ontology (GO) directed acyclic graph (DAG). These approaches contributed to improving protein analyses at the functional level. Considering the recent proliferation of these approaches, a unified theory in a well-defined mathematical framework is necessary in order to provide a theoretical basis for validating these approaches. We review the existing IC-based ontological similarity approaches developed in the context of biomedical and bioinformatics fields to propose a general framework and unified description of all these measures. We have conducted an experimental evaluation to assess the impact of IC approaches, different normalization models, and correction factors on the performance of a functional similarity metric. Results reveal that considering only parents or only children of terms when assessing information content or semantic similarity scores negatively impacts the approach under consideration. This study produces a unified framework for current and future GO semantic similarity measures and provides theoretical basics for comparing different approaches. The experimental evaluation of different approaches based on different term information content models paves the way towards a solution to the issue of scoring a term’s specificity in the GO DAG.

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

2019 ◽  
Vol 26 (1) ◽  
pp. 38-52 ◽  
Author(s):  
Dat Duong ◽  
Wasi Uddin Ahmad ◽  
Eleazar Eskin ◽  
Kai-Wei Chang ◽  
Jingyi Jessica Li
Keyword(s):  
Gene Ontology ◽  
Semantic Similarity

scholarly journals An integrated information-based similarity measurement of gene ontology terms

2015 ◽  
Vol 12 (4) ◽  
pp. 1235-1253 ◽  
Author(s):  
Shu-Bo Zhang ◽  
Jian-Huang Lai
Keyword(s):  
Gene Ontology ◽  
Semantic Similarity ◽  
Semantic Information ◽  
Gene Expression Dataset ◽  
Similarity Measurement ◽  
Depth Information ◽  
Go Terms ◽  
Validation Experiments ◽  
Integrated Information ◽  
Common Ancestors

Measuring the semantic similarity between pairs of terms in Gene Ontology (GO) can help to compare genes that can not be compared by other computational methods. In this study, we proposed an integrated information-based similarity measurement (IISM) to calculate the semantic similarity between two GO terms by taking into account multiple common ancestors that they share, and aggregating the semantic information and depth information of the non-redundant common ancestors. Our method searches for non-redundant common ancestors in an effective way. Validation experiments were conducted on both gene expression dataset and pathway dataset, and the experimental results suggest the superiority of our method against some existing methods.

scholarly journals GSAn: an alternative to enrichment analysis for annotating gene sets

2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Aaron Ayllon-Benitez ◽  
Romain Bourqui ◽  
Patricia Thébault ◽  
Fleur Mougin
Keyword(s):  
Gene Ontology ◽  
Semantic Similarity ◽  
A Priori ◽  
Similarity Measures ◽  
Enrichment Analysis ◽  
Biological Information ◽  
Underlying Structure ◽  
Gene Set ◽  
Sequencing Technologies ◽  
Gene Coverage

Abstract The revolution in new sequencing technologies is greatly leading to new understandings of the relations between genotype and phenotype. To interpret and analyze data that are grouped according to a phenotype of interest, methods based on statistical enrichment became a standard in biology. However, these methods synthesize the biological information by a priori selecting the over-represented terms and may suffer from focusing on the most studied genes that represent a limited coverage of annotated genes within a gene set. Semantic similarity measures have shown great results within the pairwise gene comparison by making advantage of the underlying structure of the Gene Ontology. We developed GSAn, a novel gene set annotation method that uses semantic similarity measures to synthesize a priori Gene Ontology annotation terms. The originality of our approach is to identify the best compromise between the number of retained annotation terms that has to be drastically reduced and the number of related genes that has to be as large as possible. Moreover, GSAn offers interactive visualization facilities dedicated to the multi-scale analysis of gene set annotations. Compared to enrichment analysis tools, GSAn has shown excellent results in terms of maximizing the gene coverage while minimizing the number of terms.

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