Modular neural network in prediction of protein function


The KIPS Transactions:PartB , Vol. 13, No. 1, pp. 1-6, Feb. 2006
10.3745/KIPSTB.2006.13.1.1,   PDF Download:

Abstract

The prediction of protein function basically make use of a protein-protein interaction map based on the concept of guilt-by-association. The method however cannot determine the functions of proteins in case that the target protein does not interact with proteins with known functions directly. This paper studies protein function prediction considering the given problem as a K-class classification problem and proposes a predictive approach utilizing a modular neural network. The proposed method uses interaction data and protein related attributes as well. The experimental results demonstrate that the proposed approach can predict the functional roles of Yeast proteins whose interaction knowledge is not known and shows better performance than the graph-based models that use protein interaction data.


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Cite this article
[IEEE Style]
D. S. Hwang, "Modular neural network in prediction of protein function," The KIPS Transactions:PartB , vol. 13, no. 1, pp. 1-6, 2006. DOI: 10.3745/KIPSTB.2006.13.1.1.

[ACM Style]
Doo Sung Hwang. 2006. Modular neural network in prediction of protein function. The KIPS Transactions:PartB , 13, 1, (2006), 1-6. DOI: 10.3745/KIPSTB.2006.13.1.1.