Predicting protein functions from PPI networks using functional aggregation

Hou, Jingyu and Chi, Xiaoxiao 2012, Predicting protein functions from PPI networks using functional aggregation, Mathematical biosciences, vol. 240, no. 1, pp. 63-69.

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Title Predicting protein functions from PPI networks using functional aggregation
Author(s) Hou, Jingyu
Chi, Xiaoxiao
Journal name Mathematical biosciences
Volume number 240
Issue number 1
Start page 63
End page 69
Total pages 7
Publisher Elsevier
Place of publication Philadelphia, Pa.
Publication date 2012
ISSN 0025-5564
1879-3134
Keyword(s) function prediction
protein interaction
Choquet-integral
Summary 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.
Language eng
Field of Research 080109 Pattern Recognition and Data Mining
080299 Computation Theory and Mathematics not elsewhere classified
Socio Economic Objective 890205 Information Processing Services (incl. Data Entry and Capture)
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2012, Elsevier
Persistent URL http://hdl.handle.net/10536/DRO/DU:30047085

Document type: Journal Article
Collection: School of Information Technology
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