Functional evaluation of domain-domain interactions and human protein interaction networks

@article{citeulike:1249013, abstract = {Motivation: Large amounts of protein and domain interaction data are being produced by experimental high-throughput techniques and computational approaches. To gain insight into the value of the provided data, we used our new similarity measure based on the Gene Ontology (GO) to evaluate the molecular functions and biological processes of interacting proteins or domains. The applied measure particularly addresses the frequent annotation of proteins or domains with multiple GO terms. Results: Using our similarity measure, we compare predicted domain-domain and human protein-protein interactions with experimentally derived interactions. The results show that our similarity measure is of significant benefit in quality assessment and confidence ranking of domain and protein networks. We also derive useful confidence score thresholds for dividing domain interaction predictions into subsets of low and high confidence. Contact: mario.albrecht@mpi-inf.mpg.de Supplementary information: Supplementary data are available at Bioinformatics online. 10.1093/bioinformatics/btm012}, author = {Schlicker, Andreas and Huthmacher, Carola and Ramirez, Fidel and Lengauer, Thomas and Albrecht, Mario }, citeulike-article-id = {1249013}, doi = {10.1093/bioinformatics/btm012}, journal = {Bioinformatics}, keywords = {domain, interaction, network, protein}, month = {April}, number = {7}, pages = {859--865}, posted-at = {2007-04-29 05:10:56}, priority = {3}, title = {Functional evaluation of domain-domain interactions and human protein interaction networks}, url = {http://dx.doi.org/10.1093/bioinformatics/btm012}, volume = {23}, year = {2007} }

TY - JOUR ID - citeulike:1249013 L3 - citeulike-article-id:1249013 TI - Functional evaluation of domain-domain interactions and human protein interaction networks JF - Bioinformatics VL - 23 IS - 7 SP - 859 EP - 865 N2 - Motivation: Large amounts of protein and domain interaction data are being produced by experimental high-throughput techniques and computational approaches. To gain insight into the value of the provided data, we used our new similarity measure based on the Gene Ontology (GO) to evaluate the molecular functions and biological processes of interacting proteins or domains. The applied measure particularly addresses the frequent annotation of proteins or domains with multiple GO terms. Results: Using our similarity measure, we compare predicted domain-domain and human protein-protein interactions with experimentally derived interactions. The results show that our similarity measure is of significant benefit in quality assessment and confidence ranking of domain and protein networks. We also derive useful confidence score thresholds for dividing domain interaction predictions into subsets of low and high confidence. Contact: mario.albrecht@mpi-inf.mpg.de Supplementary information: Supplementary data are available at Bioinformatics online. 10.1093/bioinformatics/btm012 KW - domain KW - interaction KW - network KW - protein AU - Schlicker, Andreas AU - Huthmacher, Carola AU - Ramirez, Fidel AU - Lengauer, Thomas AU - Albrecht, Mario PY - 2007/04/01/ UR - http://dx.doi.org/10.1093/bioinformatics/btm012 ER -