Group-wise Semantic Similarity Measure
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A Group-wise Semantic Similarity Measure is a Topological Semantic Similarity Measure that calculates similarities between two ontological instances based on the group's Jaccard index.
- Context:
- It can range from a Set-based Group-wise Semantic Similarity Measure, to being a Vector-based Group-wise Semantic Similarity Measure, to being a Graph-based Group-wise Semantic Similarity Measure.
- Example(s):
- Counter-Example(s):
- See: Semantic Similarity Neural Network, Ontology-based Semantic Similarity Measure, Statistical Semantic Similarity, Semantic Word Similarity Measure.
References
2021
- (Wikipedia, 2021) ⇒ https://en.wikipedia.org/wiki/Semantic_similarity#Topological_similarity Retrieved:2021-8-7.
- There are essentially two types of approaches that calculate topological similarity between ontological concepts:
- Edge-based: which use the edges and their types as the data source;
- Node-based: in which the main data sources are the nodes and their properties.
- Other measures calculate the similarity between ontological instances:
- Pairwise: measure functional similarity between two instances by combining the semantic similarities of the concepts they represent
- Groupwise: calculate the similarity directly not combining the semantic similarities of the concepts they represent
- There are essentially two types of approaches that calculate topological similarity between ontological concepts:
2009a
- (Gentleman, 2009) ⇒ R. Gentleman (2009). "Visualizing and Distances Using GO".
2009b
- (Pesquita et al., 2009 ) ⇒ Catia Pesquita, Daniel Faria, Andre O. Falcao, Phillip Lord, and Francisco M. Couto (2009). "Semantic Similarity in Biomedical Ontologies". In: PLoS Computational Biology 5(7): e1000443.
- QUOTE: Groupwise approaches do not rely on combining similarities between individual terms to calculate gene product similarity, but calculate it directly by one of three approaches: set, graph, or vector.
(...)
- QUOTE: Groupwise approaches do not rely on combining similarities between individual terms to calculate gene product similarity, but calculate it directly by one of three approaches: set, graph, or vector.
| Measure | Approach | Techniques | Weighting |
|---|---|---|---|
| Lee et al. (2004) (TO) | Graph-based | Term overlap | None |
| Mistry et al. (2008) (NTO) | Graph-based | Term overlap, Normalized | None |
| Gentleman (2005) (simLP) | Graph-based | Shared-path | None |
| Gentleman (2005) (simUI) | Graph-based | Jaccard | None |
| Martin et al. (2004) (GOToolBox) | Graph-based | Czekanowski-Dice, Jaccard | None |
| Pesquita et al. (2008) (simUI) | Graph-based | Jaccard | IC |
| Ye et al. (2005) | Graph-based | LCA, Normalized | None |
| Cho et al. (2007) | Graph-based | LCA | IC |
| Lin et al. (2004) | Graph-based | Intersection | Annotation set probability |
| Yu et al. (2007) | Graph-based | LCA | Annotation set probability |
| Sheehan et al. (2008) (SSA) | Graph-based | Resnik, Lin | Annotation set probability |
| Huang et al. (2007) | Vector-based | Kappa-statistic | None |
| Chabalier et al. (2007) | Vector-based | Cosine | IC |
2008a
- (Mistry & Pavlidis, 2008) ⇒ Meeta Mistry, and Paul Pavlidis (2008)."Gene Ontology term overlap as a measure of gene functional similarity". In: BMC Bioinformatics volume 9, Article number: 327.
2008b
- (Pesquita et al., 2008) &rArr Catia Pesquita, Daniel Faria, Hugo Bastos, Antonio EN Ferreira, Andre O Falcao, and Francisco M Couto (2008). "Metrics for GO based protein semantic similarity: a systematic evaluation". In: BMC Bioinformatics volume 9, Article number: S4.
2008c
- (Sheehan et al., 2008) ⇒ Brendan Sheehan, Aaron Quigley, Benoit Gaudin, and Simon Dobson (2008). "A relation based measure of semantic similarity for Gene Ontology annotations". In: BMC Bioinformatics. 9: 468.
2007a
- (Chabalier et al., 2007) ⇒ Julie Chabalier, Jean Mosser, and Anita Burgun (2007). "A transversal approach to predict gene product networks from ontology-based similarity". In: BMC Bioinformatics, 8: 235.
2007b
- (Cho et al., 2007) ⇒ Young-Rae Cho, Woochang Hwang, Murali Ramanathan, and Aidong Zhang (2007). "Semantic integration to identify overlapping functional modules in protein interaction networks". In: BMC Bioinformatics, 8: 265.
2007c
- (Huang et al., 2007) ⇒ Da Wei Huang, Brad T. Sherman, Qina Tan, Jack R Collins, W. Gregory Alvord, Jean Roayaei, Robert Stephens, Michael W. Baseler, H. Clifford Lane, and Richard A. Lempicki (2007). "The DAVID Gene Functional Classification Tool: a novel biological module-centric algorithm to functionally analyze large gene lists". In: Genome Biology volume 8, Article number: R183.
2007d
- (Yu et al., 2007) ⇒ Haiyuan Yu, Ronald Jansen, and Mark Gerstein (2007). “Developing a similarity measure in biological function space". In: Bioinformatics.
2005
- (Ye et al., 2005) ⇒ Ping Ye, Brian D. Peyser, Xuewen Pan, Jef D. Boeke, Forrest A. Spencer and Joel S. Bader (2005). [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1681444/ "Gene function prediction from congruent synthetic lethal interactions in yeast". In: Molecular Systems Biology 1: 2005.0026.
2004a
- (Lee et al., 2004) ⇒ Homin K. Lee, Amy K. Hsu, Jon Sajdak, Jie Qin, and Paul Pavlidis (2004)."Coexpression Analysis of Human Genes Across Many Microarray Data Sets". In: Genome Research.
2004b
- (Lin et al., 2004) ⇒ Nan Lin, Baolin Wu, Ronald Jansen, Mark Gerstein,and Hongyu Zhao (2004). "Information assessment on predicting protein-protein interactions". In: BMC Bioinformatics volume 5, Article number: 154.
2004c
- (Martin et al., 2004) ⇒ David Martin, Christine Brun, Elisabeth Remy, Pierre Mouren, Denis Thieffry, and Bernard Jacq (2004)."GOToolBox: functional analysis of gene datasets based on Gene Ontology". In: Genome Biology volume 5, Article number: R101.