ArticleBMC systems biology2018
Improving the measurement of semantic similarity by combining gene ontology and co-functional network: a random walk based approach.
Article in BMC systems biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
- Empowering the discovery of novel target-disease associations via machine learning approaches in the open targets platform.BMC bioinformatics · 2022Article
- GOntoSim: a semantic similarity measure based on LCA and common descendants.Scientific reports · 2022Article
- Pan-Cancer Bioinformatics Analysis of Gene UBE2C.Frontiers in genetics · 2022Article
- Personalized single-cell networks: a framework to predict the response of any gene to any drug for any patient.BioData mining · 2021Article
- Semantic similarity and machine learning with ontologies.Briefings in bioinformatics · 2021Review
- A Literature Review of Gene Function Prediction by Modeling Gene Ontology.Frontiers in genetics · 2020Review
- RWRNET: A Gene Regulatory Network Inference Algorithm Using Random Walk With Restart.Frontiers in genetics · 2020Article
- Predicting the Disease Genes of Multiple Sclerosis Based on Network Representation Learning.Frontiers in genetics · 2020Article
- Prioritizing candidate diseases-related metabolites based on literature and functional similarity.BMC bioinformatics · 2019Article
- TS-GOEA: a web tool for tissue-specific gene set enrichment analysis based on gene ontology.BMC bioinformatics · 2019Article
- ProbPFP: a multiple sequence alignment algorithm combining hidden Markov model optimized by particle swarm optimization with partition function.BMC bioinformatics · 2019Article
- Combining gene ontology with deep neural networks to enhance the clustering of single cell RNA-Seq data.BMC bioinformatics · 2019Article
- Predicting disease-related phenotypes using an integrated phenotype similarity measurement based on HPO.BMC systems biology · 2019Article
- A Novel Approach for Drug-Target Interactions Prediction Based on Multimodal Deep Autoencoder.Frontiers in pharmacology · 2019Article
- Predicting Parkinson's Disease Genes Based on Node2vec and Autoencoder.Frontiers in genetics · 2019Article
- Predicting circRNA-Disease Associations Based on circRNA Expression Similarity and Functional Similarity.Frontiers in genetics · 2019Article
- Exposing the Causal Effect of Body Mass Index on the Risk of Type 2 Diabetes Mellitus: A Mendelian Randomization Study.Frontiers in genetics · 2019Article
- k-Skip-n-Gram-RF: A Random Forest Based Method for Alzheimer's Disease Protein Identification.Frontiers in genetics · 2019Article
- Article
- A Similarity Regression Fusion Model for Integrating Multi-Omics Data to Identify Cancer Subtypes.Genes · 2018Article
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7 authors.
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Abstract
backgroundGene Ontology (GO) is one of the most popular bioinformatics resources. In the past decade, Gene Ontology-based gene semantic similarity has been effectively used to model gene-to-gene interactions in multiple research areas. However, most existing semantic similarity approaches rely only on GO annotations and structure, or incorporate only local interactions in the co-functional network. This may lead to inaccurate GO-based similarity resulting from the incomplete GO topology structure and gene annotations.
resultsWe present NETSIM2, a new network-based method that allows researchers to measure GO-based gene functional similarities by considering the global structure of the co-functional network with a random walk with restart (RWR)-based method, and by selecting the significant term pairs to decrease the noise information. Based on the EC number (Enzyme Commission)-based groups of yeast and Arabidopsis, evaluation test shows that NETSIM2 can enhance the accuracy of Gene Ontology-based gene functional similarity.
conclusionsUsing NETSIM2 as an example, we found that the accuracy of semantic similarities can be significantly improved after effectively incorporating the global gene-to-gene interactions in the co-functional network, especially on the species that gene annotations in GO are far from complete.
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