Loading [MathJax]/extensions/MathMenu.js
Predicting protein complexes in protein interaction networks: A supervised learning based method | IEEE Conference Publication | IEEE Xplore

Predicting protein complexes in protein interaction networks: A supervised learning based method


Abstract:

In this paper, we present a supervised learning-based method for predicting protein complexes in protein interaction network. The method extracts rich features from prote...Show More

Abstract:

In this paper, we present a supervised learning-based method for predicting protein complexes in protein interaction network. The method extracts rich features from protein interaction network to train a Regression model, which is then used for the cliques filtering, growth, and candidate complex filtering. The experimental results on several protein interaction networks show that our method outperforms other state-of-the-art protein complex detection methods.
Date of Conference: 18-21 December 2013
Date Added to IEEE Xplore: 06 February 2014
Electronic ISBN:978-1-4799-1309-1
Conference Location: Shanghai, China

I. Introduction

Protein complexes are important for understanding principles of cellular organization and function. In the post-genome era, high-throughput experimental techniques have produced a large amount of protein interactions, making it possible to predict protein complexes from the protein interaction networks (PINs). Many complex identification approaches have been proposed to detect protein complexes from the PIN, including MCODE [1], CMC [2], COACH [3]. However, most of above methods are unsupervised learning methods, which predict the protein complexes based on the pre-defined rules and can not make full use of the information of available known complexes. In this paper, we present a supervised learning based method to discover the complexes in the PIN.

References

References is not available for this document.