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Parzen Windows Based Protein Function Prediction Using Protein-Protein Interaction Data


A.M. Koura, A. H. Kamal, I. F. Abdul-Rahman


Vol. 10  No. 7  pp. 123-128


Determining protein function on a proteomic scale is a major challenge in the post-genomic era. Right now only less than half of the actual functional annotations are available for a typical proteome. The recent high-throughput bio-techniques have provided us large-scale protein? protein interaction (PPI) data, and many studies have shown that function prediction from PPI data is a promising way as proteins are likely to collaborate for a common purpose. However, the protein interaction data is very noisy, which makes the task very challenging. In this paper, a Parzen Window classifier is proposed to predict protein functions using IntAct protein interaction dataset. We present a probabilistic framework for predicting functions of unknown proteins based on incorporating Parzen Windows in the Bayesian formula. We use the leave-one-out cross validation to compare the performance. The experimental results demonstrate that our algorithm performs better than other competing methods in terms of prediction accuracy.


Parzen Windows, protein function, protein-protein interactions, Bayesian classifier