Measuring semantic similarity between Gene Ontology terms

Couto, Francisco M.; Silva, Mário J.; Coutinho, Pedro M.

doi:10.1016/j.datak.2006.05.003

Cited by 178 publications

(165 citation statements)

References 12 publications

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“…In this article, the semantic similarity between protein pairs is calculated by the Lin method (GraSM) to weigh the PPI networks (Couto et al, 2007). We develop the WCOACH (Weighted COACH) method based on the COACH algorithm and predict protein complexes in weighted PPI networks.…”

Section: Introductionmentioning

confidence: 99%

WCOACH: Protein complex prediction in weighted PPI networks

2015

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Protein complexes are aggregates of protein molecules that play important roles in biological processes. Detecting protein complexes from protein-protein interaction (PPI) networks is one of the most challenging problems in computational biology, and many computational methods have been developed to solve this problem. Generally, these methods yield high false positive rates. In this article, a semantic similarity measure between proteins, based on Gene Ontology (GO) structure, is applied to weigh PPI networks. Consequently, one of the well-known methods, COACH, has been improved to be compatible with weighted PPI networks for protein complex detection. The new method, WCOACH, is compared to the COACH, ClusterOne, IPCA, CORE, OH-PIN, HC-PIN and MCODE methods on several PPI networks such as DIP, Krogan, Gavin 2002 and MIPS. WCOACH can be applied as a fast and high-performance algorithm to predict protein complexes in weighted PPI networks. All data and programs are freely available at http://bioinformatics. aut.ac.ir/wcoach.

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Section: Introductionmentioning

confidence: 99%

WCOACH: Protein complex prediction in weighted PPI networks

2015

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“…In the health domain, the researchers are mainly concerned with seeking similar health science terms. In the field of bioinformatics, the focus is on measuring the similarity between concepts from the gene ontology [16][17][18][19]. In the field of web services, the researches concentrate on semantic service discovery [20].…”

Section: Introductionmentioning

confidence: 99%

“…Studies have shown that most people agree on the relative semantic relatedness of most pairs of concepts [2,3]. Therefore, many technologies have been developed to date in order to precisely measure the extent of similarity relatedness and similarity between concepts in multiple disciplines, such as information retrieval (IR) [4][5][6][7][8][9], natural language processing (NLP) [10][11][12][13], linguistics [14], health informatics [15], bioinformatics [1,[16][17][18][19], web services [20], ontology extraction/matching [21][22][23] and other fields. In the fields of IR and NLP, the researches primarily focus on word sense disambiguation [9,10], multimodal document retrieval [24], text segmentation [7,12] and query preciseness enhancement [5,6].…”

Section: Introductionmentioning

confidence: 99%

A context‐aware semantic similarity model for ontology environments

Dong

Hussain

Chang

2010

Concurrency and Computation

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SUMMARYWhile many researchers have contributed to the field of semantic similarity models so far, we find that most of the models are designed for the semantic network environment. When applying the semantic similarity model within the semantic-rich ontology environment, two issues are observed: (1) most of the models ignore the context of ontology concepts and (2) most of the models ignore the context of relations. Therefore, in this paper, we present a solution for the two issues, including a novel ontology conversion process and a context-aware semantic similarity model, by considering the factors of both the context of concepts and relations, and the ontology structure. Furthermore, in order to evaluate this model, we compare its performance with that of several existing models' performance in a large-scale knowledge base, and the evaluation result preliminarily proves the technical advantage of our model in ontology environments. Conclusions and future works are described in the final section.

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“…The ambiguous vocabulary of the data leads to difficulties in querying metadata systems to search for information. Incorrect outcomes could be obtained either by returning too general terms or too deep terms according to the search criteria (Shatkay and Feldman, 2003;Pérez et al, 2004;Couto et al, 2007). However, a well designed semantic structure can enhance the metadata system by providing better precision in the search process.…”

Section: Ontology-based Metadatamentioning

confidence: 99%

Semantic enabled metadata management in PetaShare

Wang

Huang

Akturk

et al. 2009

IJGUC

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We designed a semantic enabled metadata framework using ontology for multi-disciplinary and multi-institutional large scale scientific data sets in a Data Grid setting. Two main issues are addressed: data integration for semantically and physically heterogeneous distributed knowledge stores, and semantic reasoning for data verification and inference in such a setting. This framework enables data interoperability between otherwise semantically incompatible data sources, cross-domain query capabilities and multi-source knowledge extraction. In this paper, we present the basic system architecture for this framework, as well as an initial implementation. We also analyze a real-life scenario and show integration of our framework into the PetaShare Data Grid where multi-disciplinary data archives are geographically distributed across six research institutions in Louisiana.

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Measuring semantic similarity between Gene Ontology terms

Cited by 178 publications

References 12 publications

WCOACH: Protein complex prediction in weighted PPI networks

WCOACH: Protein complex prediction in weighted PPI networks

A context‐aware semantic similarity model for ontology environments

Semantic enabled metadata management in PetaShare

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