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pmid:25061067
Predicting protein phosphorylation from gene expression: top methods from the IMPROVER Species Translation Challenge.
Adel Dayarian · Roberto Romero · Zhiming Wang · Michael Biehl · Erhan Bilal · Sahand Hormoz · Pablo Meyer · Raquel Norel · Kahn Rhrissorrakrai · Gyan Bhanot · Feng Luo · Adi L Tarca
Bioinformatics (Oxford, England) · 2015 doi:10.1093/bioinformatics/btu490
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Complete file
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The intra-species protein phosphorylation challenge organized by the IMPROVER consortium provided the framework to identify the best approaches to address this issue.\nRESULTS: Rat lung epithelial cells were treated with 52 stimuli, and gene expression and phosphorylation levels were measured. Competing teams used gene expression data from 26 stimuli to develop protein phosphorylation prediction models and were ranked based on prediction performance for the remaining 26 stimuli. Three teams were tied in first place in this challenge achieving a balanced accuracy of about 70%, indicating that gene expression is only moderately predictive of protein phosphorylation. In spite of the similar performance, the approaches used by these three teams, described in detail in this article, were different, with the average number of predictor genes per phosphoprotein used by the teams ranging from 3 to 124. However, a significant overlap of gene signatures between teams was observed for the majority of the proteins considered, while Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were enriched in the union of the predictor genes of the three teams for multiple proteins.\nAVAILABILITY AND IMPLEMENTATION: Gene expression and protein phosphorylation data are available from ArrayExpress (E-MTAB-2091). Software implementation of the approach of Teams 49 and 75 are available at http://bioinformaticsprb.med.wayne.edu and http://people.cs.clemson.edu/∼luofeng/sbv.rar, respectively.\nCONTACT: gyanbhanot@gmail.com or luofeng@clemson.edu or atarca@med.wayne.edu\nSUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.", "claims": [{"id": "claim-target-result", "statement": "Three teams tied for first place using a balanced accuracy of about 70%, indicating that gene expression is only moderately predictive of protein phosphorylation.", "kind": "quantitative", "target": {"value": 70.0, "unit": "per cent", "tolerance": null, "source": "Three teams were tied in first place in this challenge achieving a balanced accuracy of about 70%, indicating that gene expression is only moderately predictive of protein phosphorylation."}, "evidence": [{"asset": "source:pmid:25061067", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Three teams were tied in first place in this challenge achieving a balanced accuracy of about 70%, indicating that gene expression is only moderately predictive of protein phosphorylation."}, {"asset": "source:pmid:25061067", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Gene expression and protein phosphorylation data are available from ArrayExpress (E-MTAB-2091)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "Three teams tied for first place using a balanced accuracy of about 70%, indicating that gene expression is only moderately predictive of protein phosphorylation.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:25061067"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "blocked", "blocked_on": "data:accession_absent", "description": "The GEO series accession resolves and its sample accessions are listed. 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