ArticlePloS one2013
Combining phylogenetic profiling-based and machine learning-based techniques to predict functional related proteins.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Protein Interface Complementarity and Gene Duplication Improve Link Prediction of Protein-Protein Interaction Network.Frontiers in genetics · 2020Article
- Computational identification of protein-protein interactions in model plant proteomes.Scientific reports · 2019Article
- mACPpred: A Support Vector Machine-Based Meta-Predictor for Identification of Anticancer Peptides.International journal of molecular sciences · 2019Article
- PTIR: Predicted Tomato Interactome Resource.Scientific reports · 2016Article
- Accurate prediction of nuclear receptors with conjoint triad feature.BMC bioinformatics · 2015Article
- Insights into the species-specific TLR4 signaling mechanism in response to Rhodobacter sphaeroides lipid A detection.Scientific reports · 2015Article
- Protein-protein interaction detection: methods and analysis.International journal of proteomics · 2014Review
- A regulatory similarity measure using the location information of transcription factor binding sites in Saccharomyces cerevisiae.BMC systems biology · 2014Article
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3 authors.
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Abstract
Annotating protein functions and linking proteins with similar functions are important in systems biology. The rapid growth rate of newly sequenced genomes calls for the development of computational methods to help experimental techniques. Phylogenetic profiling (PP) is a method that exploits the evolutionary co-occurrence pattern to identify functional related proteins. However, PP-based methods delivered satisfactory performance only on prokaryotes but not on eukaryotes. This study proposed a two-stage framework to predict protein functional linkages, which successfully enhances a PP-based method with machine learning. The experimental results show that the proposed two-stage framework achieved the best overall performance in comparison with three PP-based methods.
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