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Predicting protein functions from PPI networks using functional aggregation
Predicting protein functions computationally from massive protein–protein interaction (PPI) data generated by high-throughput technology is one of the challenges and fundamental problems in the post-genomic era. Although there have been many approaches developed for computationally predicting protein functions, the mutual correlations among proteins in terms of protein functions have not been thoroughly investigated and incorporated into existing prediction methods, especially in voting based prediction methods. In this paper, we propose an innovative method to predict protein functions from PPI data by aggregating the functional correlations among relevant proteins using the Choquet-Integral in fuzzy theory. This functional aggregation measures the real impact of each relevant protein function on the final prediction results, and reduces the impact of repeated functional information on the prediction. Accordingly, a new protein similarity and a new iterative prediction algorithm are proposed in this paper. The experimental evaluations on real PPI datasets demonstrate the effectiveness of our method.
History
Journal
Mathematical biosciencesVolume
240Issue
1Pagination
63 - 69Publisher
ElsevierLocation
Philadelphia, Pa.Publisher DOI
ISSN
0025-5564eISSN
1879-3134Language
engPublication classification
C1 Refereed article in a scholarly journalCopyright notice
2012, ElsevierUsage metrics
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