TY - JOUR
T1 - Whole-proteome prediction of protein function via graph-theoretic analysis of interaction maps
AU - Nabieva, Elena
AU - Jim, Kam
AU - Agarwal, Amit
AU - Chazelle, Bernard
AU - Singh, Mona
N1 - Funding Information:
M.S. thanks the NSF for PECASE grant MCB-0093399, DARPA for grant MDA972-00-1-0031 and NIH for grant PO1-CA-041086. B.C. thanks the NSF for grant CCR-0306283, DARPA for ARO grant DAAH04-96-1-0181 and the NEC Research Institute. The authors thank the members of the Singh group, especially Carl Kingsford, for many helpful discussions.
PY - 2005/6
Y1 - 2005/6
N2 - Motivation: Determining protein function is one of he most important problems in the post-genomic era. For the typical proteome, there are no functional annotations for one-third or more of its proteins. Recent high-throughput experiments have determined proteome-scale protein physical interaction maps for several organisms. These physical interactions are complemented by an abundance of data about other types of functional relationships between proteins, including genetic interactions, knowledge about co-expression and shared evolutionary history. Taken together, these pairwise linkages can be used to build whole-proteome protein interaction maps. Results: We develop a network-flow based algorithm, FunctionalFlow, that exploits the underlying structure of protein interaction maps in order to predict protein function. In cross-validation testing on the yeast proteome, we show that FunctionalFlow has improved performance over previous methods in predicting the function of proteins with few (or no) annotated protein neighbors. By comparing several methods that use protein interaction maps to predict protein function, we demonstrate that FunctionalFlow performs well because it takes advantage of both network topology and some measure of locality. Finally, we show that performance can be improved substantially as we consider multiple data sources and use them to create weighted interaction networks.
AB - Motivation: Determining protein function is one of he most important problems in the post-genomic era. For the typical proteome, there are no functional annotations for one-third or more of its proteins. Recent high-throughput experiments have determined proteome-scale protein physical interaction maps for several organisms. These physical interactions are complemented by an abundance of data about other types of functional relationships between proteins, including genetic interactions, knowledge about co-expression and shared evolutionary history. Taken together, these pairwise linkages can be used to build whole-proteome protein interaction maps. Results: We develop a network-flow based algorithm, FunctionalFlow, that exploits the underlying structure of protein interaction maps in order to predict protein function. In cross-validation testing on the yeast proteome, we show that FunctionalFlow has improved performance over previous methods in predicting the function of proteins with few (or no) annotated protein neighbors. By comparing several methods that use protein interaction maps to predict protein function, we demonstrate that FunctionalFlow performs well because it takes advantage of both network topology and some measure of locality. Finally, we show that performance can be improved substantially as we consider multiple data sources and use them to create weighted interaction networks.
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U2 - 10.1093/bioinformatics/bti1054
DO - 10.1093/bioinformatics/bti1054
M3 - Article
C2 - 15961472
AN - SCOPUS:29144442904
SN - 1367-4803
VL - 21
SP - i302-i310
JO - Bioinformatics
JF - Bioinformatics
IS - SUPPL. 1
ER -