ArticleBMC medical genomics2016
Fuzzy-FishNET: a highly reproducible protein complex-based approach for feature selection in comparative proteomics.
Article in BMC medical genomics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Integration of protein context improves protein-based COVID-19 patient stratification.Clinical proteomics · 2022Article
- Computational advances of tumor marker selection and sample classification in cancer proteomics.Computational and structural biotechnology journal · 2020Review
- Protein complex-based analysis is resistant to the obfuscating consequences of batch effects --- a case study in clinical proteomics.BMC genomics · 2017Article
- 2016 update on APBioNet's annual international conference on bioinformatics (InCoB).BMC genomics · 2016Article
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1 author.
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Abstract
backgroundThe hypergeometric enrichment analysis approach typically fares poorly in feature-selection stability due to its upstream reliance on the t-test to generate differential protein lists before testing for enrichment on a protein complex, subnetwork or gene group.
methodsSwapping the t-test in favour of a fuzzy rank-based weight system similar to that used in network-based methods like Quantitative Proteomics Signature Profiling (QPSP), Fuzzy SubNets (FSNET) and paired FSNET (PFSNET) produces dramatic improvements.
resultsThis approach, Fuzzy-FishNET, exhibits high precision-recall over three sets of simulated data (with simulated protein complexes) while excelling in feature-selection reproducibility on real data (based on evaluation with real protein complexes). Overlap comparisons with PFSNET shows Fuzzy-FishNET selects the most significant complexes, which are also strongly class-discriminative. Cross-validation further demonstrates Fuzzy-FishNET selects class-relevant protein complexes.
conclusionsBased on evaluation with simulated and real datasets, Fuzzy-FishNET is a significant upgrade of the traditional hypergeometric enrichment approach and a powerful new entrant amongst comparative proteomics analysis methods.
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