by Nan Lin, Baolin Wu, Ronald Jansen, Mark Gerstein, Hongyu Zhao
Abstract:
Identifying protein-protein interactions is fundamental for understanding the molecular machinery of the cell. Proteome-wide studies of protein-protein interactions are of significant value, but the high-throughput experimental technologies suffer from high rates of both false positive and false negative predictions. In addition to high-throughput experimental data, many diverse types of genomic data can help predict protein-protein interactions, such as mRNA expression, localization, essentiality, and functional annotation. Evaluations of the information contributions from different evidences help to establish more parsimonious models with comparable or better prediction accuracy, and to obtain biological insights of the relationships between protein-protein interactions and other genomic information.
Reference:
Information assessment on predicting protein-protein interactions. (Nan Lin, Baolin Wu, Ronald Jansen, Mark Gerstein, Hongyu Zhao), In BMC Bioinformatics, volume 5, 2004.
Bibtex Entry:
@article{Lin2004,
abstract = {Identifying protein-protein interactions is fundamental for understanding the molecular machinery of the cell. Proteome-wide studies of protein-protein interactions are of significant value, but the high-throughput experimental technologies suffer from high rates of both false positive and false negative predictions. In addition to high-throughput experimental data, many diverse types of genomic data can help predict protein-protein interactions, such as mRNA expression, localization, essentiality, and functional annotation. Evaluations of the information contributions from different evidences help to establish more parsimonious models with comparable or better prediction accuracy, and to obtain biological insights of the relationships between protein-protein interactions and other genomic information.},
author = {Lin, Nan and Wu, Baolin and Jansen, Ronald and Gerstein, Mark and Zhao, Hongyu},
doi = {10.1186/1471-2105-5-154},
issn = {1471-2105},
journal = {BMC Bioinformatics},
keywords = {Artificial Intelligence,Databases, Protein,Fungal Proteins,Fungal Proteins: metabolism,Genome, Fungal,Genomics,Genomics: methods,Logistic Models,Models, Genetic,Predictive Value of Tests,Protein Interaction Mapping,Protein Interaction Mapping: methods,Protein Interaction Mapping: statistics \& numerica,Proteome,Proteome: metabolism,SML-LIB-BIBLIO,lang:ENG},
mendeley-tags = {SML-LIB-BIBLIO,lang:ENG},
month = oct,
pages = {154},
pmid = {15491499},
title = {{Information assessment on predicting protein-protein interactions.}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=529436\&tool=pmcentrez\&rendertype=abstract},
volume = {5},
year = {2004}
}