title: Gaining confidence in high-throughput protein interaction networks.
abstract: Although genome-scale technologies have benefited from statistical measures of data quality, extracting biologically relevant pathways from high-throughput proteomics data remains a challenge. Here we develop a quantitative method for evaluating proteomics data. We present a logistic regression approach that uses statistical and topological descriptors to predict the biological relevance of protein-protein interactions obtained from high-throughput screens for yeast. Other sources of information, including mRNA expression, genetic interactions and database annotations, are subsequently used to validate the model predictions without bias or cross-pollution. Novel topological statistics show hierarchical organization of the network of high-confidence interactions: protein complex interactions extend one to two links, and genetic interactions represent an even finer scale of organization. Knowledge of the maximum number of links that indicates a significant correlation between protein pairs (correlation distance) enables the integrated analysis of proteomics data with data from genetics and gene expression. The type of analysis presented will be essential for analyzing the growing amount of genomic and proteomics data in model organisms and humans.
authors.affiliation: Department of Biomedical Engineering, 201C Clark Hall, Johns Hopkins University, 3400 N. Charles St., Baltimore, Maryland 21218, USA. joel.bader@jhu.edu
authors.forename: Joel S
authors.initials: JS
authors.lastname: Bader
authors.forename: Amitabha
authors.initials: A
authors.lastname: Chaudhuri
authors.forename: Jonathan M
authors.initials: JM
authors.lastname: Rothberg
authors.forename: John
authors.initials: J
authors.lastname: Chant
mesh_terms: D000465:Algorithms; D000818:Animals; D002455:Cell Division; D016000:Cluster Analysis; D019992:Databases as Topic; D005786:Gene Expression Regulation; D016678:Genome; D006801:Humans; D051379:Mice; D008962:Models, Theoretical; D011233:Precipitin Tests / Q000379:methods; D011485:Protein Binding; D020543:Proteome; D012333:RNA, Messenger / Q000378:metabolism; D012044:Regression Analysis; D012984:Software; D013223:Statistics as Topic
chemical_list: D020543:Proteome; D012333:RNA, Messenger