Graph-based modeling for protein function prediction


The KIPS Transactions:PartB , Vol. 12, No. 2, pp. 209-214, Apr. 2005
10.3745/KIPSTB.2005.12.2.209,   PDF Download:

Abstract

The use of protein interaction data is highly reliable for predicting functions to proteins without functions in proteomics study. The computational studies on protein function prediction are mostly based on the concept of guilt-by-association and utilize large-scale interaction map from revealed protein-protein interaction data. This study compares graph-based approaches such as neighbor-counting and X^2-statistics methods using protein-protein interaction data and proposes an approach that is effective in analyzing large-scale protein interaction data. The proposed approach is also based protein interaction map but sequence similarity and heuristic knowledge to make prediction results more reliable. The test result of the proposed approach is given for KDD Cup 2001 competition data along with those of neighbor-counting and x^2-statics, method,


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Cite this article
[IEEE Style]
D. S. Hwang and J. Y. Jung, "Graph-based modeling for protein function prediction," The KIPS Transactions:PartB , vol. 12, no. 2, pp. 209-214, 2005. DOI: 10.3745/KIPSTB.2005.12.2.209.

[ACM Style]
Doo Sung Hwang and Jae Young Jung. 2005. Graph-based modeling for protein function prediction. The KIPS Transactions:PartB , 12, 2, (2005), 209-214. DOI: 10.3745/KIPSTB.2005.12.2.209.