A new method to measure the semantic similarity of GO terms

By J. Wang, Zhidian Du, Rapeeporn Payattakool, P. Yu, Chin-Fu Chen. Bioinform., 2007

634 citations recorded by the source corpus.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/bfbca14004c289c6bff4fd476b197ba090b67655

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In Molecular Biology

1. Guangchuang Yu
2. Qing‐Yu He
3. Feixiong Cheng
4. Shanfeng Zhu
5. István A. Kovács
6. Li-Gen Wang
7. Min Wu
8. Hong Yan

Citations in the corpus

• Network-based prediction of drug combinations (2019, methodology): “We computed GO similarity S GO ( a, b ) for each pair of drug target-coding genes a and b using a graph-based semantic similarity measure algorithm 52 implemented in an R package, named GOSem-Sim 53 .”
• DOSE: an R/Bioconductor package for disease ontology semantic and enrichment analysis (2015, methodology): “…content based algorithms proposed by Resnik (Resnik, 1999), Lin (Lin, 1998), Jiang and Conrath (Jiang and Conrath, 1997) and Schlicker (Schlicker et al. , 2006), respectively, and one graph based algorithm proposed by Wang (Wang et al. , 2007) to measure the semantic similarity among DO terms.”
• The Influence of Feature Selection Methods on Accuracy, Stability and Interpretability of Molecular Signatures (2011, methodology): “…from each signature the list of 10 GO terms with the smallest p-values, and compared the two lists of GO terms by the similarity measure of Wang et al. (2007) which takes into account not only the overlap between the lists but also the relationships between GO BP. Finally, to assess the…”
• Ieee/acm Transactions on Computational Biology and Bioinformatics (neutral): “We use the GO similarity measure proposed in [26] to compute ) , ( P P T T sim  .”
• A core-attachment based method to detect protein complexes in PPI networks (2009, methodology): “In our experiments, functional similarity between two proteins is calculated based on the method in [36] and expressional correlation is measured by the Pearson correlation coefficient.”
• Multiconstrained gene clustering based on generalized projections (2010, methodology): “To evaluate MGC methods for gene clustering, we generate two different sets of reference gene clusters with true cluster labels from KEGG [37] and SGD (Saccharomyces Genome Database) http://www.yeastgen-ome.org/ referred to as KEGG clusters [12] and SGD clusters [19], respectively.”