Protein–protein interaction inference based on semantic similarity of Gene Ontology terms

Zhang, Shubo; Tang, Qiangrong

doi:10.1016/j.jtbi.2016.04.020

Cited by 57 publications

(40 citation statements)

References 37 publications

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“…Since 2003, when Lord et al [28] introduced the idea of ontology-based semantic similarity in the gene ontology (GO), several results have been achieved using this technique, proving beyond doubt that it is sound and useful and has real-life applications. In genomics and proteomics, semantic similarity based on GO has been used to (i) cluster proteins [37], (ii) find protein-protein interactions [38], (iii) interpret microarray data [39], (iv) predict protein functions [40], (v) prioritise candidate disease genes [41], etc. Other uses outside GO include predicting disease-related phenotypes [42] and predicting clinical diagnosis from a set of phenotype abnormalities [43].…”

Section: Applicationsmentioning

confidence: 99%

Semantic Similarity in Cheminformatics

Ferreira¹,

Couto²

2020

Cheminformatics and Its Applications

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Similarity in chemistry has been applied to a variety of problems: to predict biochemical properties of molecules, to disambiguate chemical compound references in natural language, to understand the evolution of metabolic pathways, to predict drug-drug interactions, to predict therapeutic substitution of antibiotics, to estimate whether a compound is harmful, etc. While measures of similarity have been created that make use of the structural properties of the molecules, some ontologies (the Chemical Entities of Biological Interest (ChEBI) being one of the most relevant) capture chemistry knowledge in machine-readable formats and can be used to improve our notions of molecular similarity. Ontologies in the biomedical domain have been extensively used to compare entities of biological interest, a technique known as ontology-based semantic similarity. This has been applied to various biologically relevant entities, such as genes, proteins, diseases, and anatomical structures, as well as in the chemical domain. This chapter introduces the fundamental concepts of ontology-based semantic similarity, its application in cheminformatics, its relevance in previous studies, and future potential. It also discusses the existing challenges in this area, tracing a parallel with other domains, particularly genomics, where this technique has been used more often and for longer.

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Section: Applicationsmentioning

confidence: 99%

Semantic Similarity in Cheminformatics

Ferreira¹,

Couto²

2020

Cheminformatics and Its Applications

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“…is a set of edges and W is a set of weights containing biological information from von Mering data and Gene Ontology ( Consortium 2004 ;Zhang and Tang, 2016 ). In our paper, GO with three functional categories, i.e., biological processes (BP), molecular functions (MF) and cellular components (CC) is employed to present biological information of the protein.…”

Section: Construction Of Weighted Graphmentioning

confidence: 99%

Protein complex identification based on weighted PPI network with multi-source information

Zheng

2019

Journal of Theoretical Biology

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“…In addition, to analyze the biological activity of proteins, GObased semantic similarity creates an evolutionary orientation in PPI [58][59][60][61][62][63]. GO annotation-based semantic similarity has been regarded as one of the most powerful indicators for interaction [23,64].…”

Section: Computational Approachesmentioning

confidence: 99%

Review of computational methods for virus–host protein interaction prediction: a case study on novel Ebola–human interactions

Dutta

Kundu

Basu

et al. 2017

Briefings in Functional Genomics

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Identification of potential virus-host interactions is useful and vital to control the highly infectious virus-caused diseases. This may contribute toward development of new drugs to treat the viral infections. Recently, database records of clinically and experimentally validated interactions between a small set of human proteins and Ebola virus (EBOV) have been published. Using the information of the known human interaction partners of EBOV, our main objective is to identify a set of proteins that may interact with EBOV proteins. Here, we first review the state-of-the-art, computational methods used for prediction of novel virus-host interactions for infectious diseases followed by a case study on EBOV-human interactions. The assessment result shows that the predicted human host proteins are highly similar with known human interaction partners of EBOV in the context of structure and semantics and are responsible for similar biochemical activities, pathways and host-pathogen relationships.

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Protein–protein interaction inference based on semantic similarity of Gene Ontology terms

Cited by 57 publications

References 37 publications

Semantic Similarity in Cheminformatics

Semantic Similarity in Cheminformatics

Protein complex identification based on weighted PPI network with multi-source information

Review of computational methods for virus–host protein interaction prediction: a case study on novel Ebola–human interactions

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