Gene Semantic Similarity Measure

AKA: Genes Functional Similarity Measure.
Example(s):
- a GO-based Semantic Similarity Measure such as:
- a Gene Semantic Similarity Score,
- …
Counter-Example(s):
See: Semantic Similarity Score, GO Ontology, Protein-Protein Interaction Dataset, Protein-Protein Interaction Network, Word Relatedness Measure.

References

(Zhao & Wang, 2018) ⇒ Chenguang Zhao, and Zheng Wang (2018)."GOGO: An Improved Algorithm to Measure the Semantic Similarity between Gene Ontology Terms". In: Scientific Reports volume 8, Article number: 15107.
- QUOTE: Measuring the semantic similarity between Gene Ontology (GO) terms is an essential step in functional bioinformatics research. We implemented a software named GOGO for calculating the semantic similarity between GO terms. GOGO has the advantages of both information-content-based and hybrid methods, such as Resnik’s and Wang’s methods. Moreover, GOGO is relatively fast and does not need to calculate information content (IC) from a large gene annotation corpus but still has the advantage of using IC. This is achieved by considering the number of children nodes in the GO directed acyclic graphs when calculating the semantic contribution of an ancestor node giving to its descendent nodes. GOGO can calculate functional similarities between genes and then cluster genes based on their functional similarities. Evaluations performed on multiple pathways retrieved from the saccharomyces genome database (SGD) show that GOGO can accurately and robustly cluster genes based on functional similarities.

(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: However, based on the few comparative studies that exist, we can identify the most successful measures so far in the three main applications of GO-based semantic similarity: function prediction/validation, protein–protein interaction prediction/validation, and cellular location prediction (see Table 6).

**Table 6:** Best measures for the main applications of GO-based semantic similarity measures. ^aIdentified by sequence similarity. p/v, prediction/validation.
Application	Best Measure	Reference
Function^a p/v	BMA(Resnik)/simGIC	Pesquita et al. (2008)
Protein-protein interaction p/v	Max(Resnik)	Guo et al. (2006), Xu et al., (2008)
Cellular location prediction	SUM(EM)	Lei & Dai (2006)

(Gentleman, 2009) ⇒ R. Gentleman (2009). "Visualizing and Distances Using GO".
- QUOTE: The relationships between different GO terms, within a specific ontology are represented in the form of a directed acyclic graph. The leaves of this graph represent the most specific terms and their are edges from a specific term (child) to all less specific terms (each is a parent). The induced GO graph is the graph that obtains from taking a set of GO terms and finding all parents of those terms, and so on until the root node has been obtained.