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Progressive clustering based method for protein function prediction

Saini, Ashish and Hou, Jingyu 2013, Progressive clustering based method for protein function prediction, Bulletin of mathematical biology, vol. 75, no. 2, pp. 331-350, doi: 10.1007/s11538-013-9809-6.

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Title Progressive clustering based method for protein function prediction
Author(s) Saini, Ashish
Hou, JingyuORCID iD for Hou, Jingyu
Journal name Bulletin of mathematical biology
Volume number 75
Issue number 2
Start page 331
End page 350
Total pages 20
Publisher Springer
Place of publication Berlin, Germany
Publication date 2013
ISSN 1522-9602
Keyword(s) clustering
protein function prediction
protein interaction
Summary In recent years, significant effort has been given to predicting protein functions from protein interaction data generated from high throughput techniques. However, predicting protein functions correctly and reliably still remains a challenge. Recently, many computational methods have been proposed for predicting protein functions. Among these methods, clustering based methods are the most promising. The existing methods, however, mainly focus on protein relationship modeling and the prediction algorithms that statically predict functions from the clusters that are related to the unannotated proteins. In fact, the clustering itself is a dynamic process and the function prediction should take this dynamic feature of clustering into consideration. Unfortunately, this dynamic feature of clustering is ignored in the existing prediction methods. In this paper, we propose an innovative progressive clustering based prediction method to trace the functions of relevant annotated proteins across all clusters that are generated through the progressive clustering of proteins. A set of prediction criteria is proposed to predict functions of unannotated proteins from all relevant clusters and traced functions. The method was evaluated on real protein interaction datasets and the results demonstrated the effectiveness of the proposed method compared with representative existing methods.
Language eng
DOI 10.1007/s11538-013-9809-6
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
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Document type: Journal Article
Collection: School of Information Technology
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Created: Tue, 27 Aug 2013, 12:02:05 EST

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