ArticleJournal of the Royal Society, Interface2012
A novel method to quantify gene set functional association based on gene ontology.
Article in Journal of the Royal Society, Interface, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Machine learning approaches for predicting the small molecule-miRNA associations: a comprehensive review.Molecular diversity · 2025Review
- AMCSMMA: Predicting Small Molecule-miRNA Potential Associations Based on Accurate Matrix Completion.Cells · 2023Article
- Article
- Combining non-negative matrix factorization with graph Laplacian regularization for predicting drug-miRNA associations based on multi-source information fusion.Frontiers in pharmacology · 2023Article
- A message passing framework with multiple data integration for miRNA-disease association prediction.Scientific reports · 2022Article
- Identifying SM-miRNA associations based on layer attention graph convolutional network and matrix decomposition.Frontiers in molecular biosciences · 2022Article
- Drug repositioning based on the target microRNAs using bilateral-inductive matrix completion.Molecular genetics and genomics : MGG · 2020Article
- SNMFSMMA: using symmetric nonnegative matrix factorization and Kronecker regularized least squares to predict potential small molecule-microRNA association.RNA biology · 2020Article
- Identifying Small Molecule-miRNA Associations Based on Credible Negative Sample Selection and Random Walk.Frontiers in bioengineering and biotechnology · 2020Article
- In Silico Prediction of Small Molecule-miRNA Associations Based on the HeteSim Algorithm.Molecular therapy. Nucleic acids · 2019Article
- Inferring potential small molecule-miRNA association based on triple layer heterogeneous network.Journal of cheminformatics · 2018Article
- Prediction of Potential Small Molecule-Associated MicroRNAs Using Graphlet Interaction.Frontiers in pharmacology · 2018Article
- miRDDCR: a miRNA-based method to comprehensively infer drug-disease causal relationships.Scientific reports · 2017Article
- Integration of Multiple Genomic and Phenotype Data to Infer Novel miRNA-Disease Associations.PloS one · 2016Article
- Prioritising risk pathways of complex human diseases based on functional profiling.European journal of human genetics : EJHG · 2013Article
- mirTarPri: improved prioritization of microRNA targets through incorporation of functional genomics data.PloS one · 2013Article
- Systematically characterizing and prioritizing chemosensitivity related gene based on Gene Ontology and protein interaction network.BMC medical genomics · 2012Article
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11 authors.
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Abstract
Numerous gene sets have been used as molecular signatures for exploring the genetic basis of complex disorders. These gene sets are distinct but related to each other in many cases; therefore, efforts have been made to compare gene sets for studies such as those evaluating the reproducibility of different experiments. Comparison in terms of biological function has been demonstrated to be helpful to biologists. We improved the measurement of semantic similarity to quantify the functional association between gene sets in the context of gene ontology and developed a web toolkit named Gene Set Functional Similarity (GSFS; http://bioinfo.hrbmu.edu.cn/GSFS). Validation based on protein complexes for which the functional associations are known demonstrated that the GSFS scores tend to be correlated with sequence similarity scores and that complexes with high GSFS scores tend to be involved in the same functional catalogue. Compared with the pairwise method and the annotation method, the GSFS shows better discrimination and more accurately reflects the known functional catalogues shared between complexes. Case studies comparing differentially expressed genes of prostate tumour samples from different microarray platforms and identifying coronary heart disease susceptibility pathways revealed that the method could contribute to future studies exploring the molecular basis of complex disorders.
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