ArticlePloS one2011
Predicting protein phenotypes based on protein-protein interaction network.
Article in PloS one, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
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- A Random Walk-Based Method to Identify Candidate Genes Associated With Lymphoma.Frontiers in genetics · 2021Article
- Inferring novel genes related to oral cancer with a network embedding method and one-class learning algorithms.Gene therapy · 2019Article
- Broad Substrate-Specific Phosphorylation Events Are Associated With the Initial Stage of Plant Cell Wall Recognition inFrontiers in microbiology · 2019Article
- Ontology-based validation and identification of regulatory phenotypes.Bioinformatics (Oxford, England) · 2018Article
- Use of Laplacian Heat Diffusion Algorithm to Infer Novel Genes With Functions Related to Uveitis.Frontiers in genetics · 2018Article
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- Investigating genetic characteristics of hepatitis B virus-associated and -non-associated hepatocellular carcinoma.Genetics research · 2016Article
- Identifying novel protein phenotype annotations by hybridizing protein-protein interactions and protein sequence similarities.Molecular genetics and genomics : MGG · 2016Article
- The Use of Protein-Protein Interactions for the Analysis of the Associations between PM2.5 and Some Diseases.BioMed research international · 2016Article
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- Identification of novel thyroid cancer-related genes and chemicals using shortest path algorithm.BioMed research international · 2015Article
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- Identification of age-related macular degeneration related genes by applying shortest path algorithm in protein-protein interaction network.BioMed research international · 2013Article
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- Prediction of phenotype-associated genes via a cellular network approach: a Candida albicans infection case study.PloS one · 2012Article
- Predicting biological functions of compounds based on chemical-chemical interactions.PloS one · 2011Article
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4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIdentifying associated phenotypes of proteins is a challenge of the modern genetics since the multifactorial trait often results from contributions of many proteins. Besides the high-through phenotype assays, the computational methods are alternative ways to identify the phenotypes of proteins. METHODOLOGY/PRINCIPAL
findingsHere, we proposed a new method for predicting protein phenotypes in yeast based on protein-protein interaction network. Instead of only the most likely phenotype, a series of possible phenotypes for the query protein were generated and ranked according to the tethering potential score. As a result, the first order prediction accuracy of our method achieved 65.4% evaluated by Jackknife test of 1,267 proteins in budding yeast, much higher than the success rate (15.4%) of a random guess. And the likelihood of the first 3 predicted phenotypes including all the real phenotypes of the proteins was 70.6%. CONCLUSIONS/SIGNIFICANCE: The candidate phenotypes predicted by our method provided useful clues for the further validation. In addition, the method can be easily applied to the prediction of protein associated phenotypes in other organisms.
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