Application of three graph Laplacian based semi-supervised learning methods to protein function prediction problem

TL;DR

This study applies three graph Laplacian based semi-supervised learning methods to predict yeast protein functions by integrating multiple biological networks, demonstrating improved accuracy over individual networks.

Contribution

It introduces a simple fixed-weight network integration approach and compares three Laplacian-based semi-supervised methods on combined biological data.

Findings

Un-normalized and symmetric normalized methods outperform random walk method.

Integrated network results are more accurate than individual networks.

Simple fixed-weight combination does not reduce accuracy.

Abstract

Protein function prediction is the important problem in modern biology. In this paper, the un-normalized, symmetric normalized, and random walk graph Laplacian based semi-supervised learning methods will be applied to the integrated network combined from multiple networks to predict the functions of all yeast proteins in these multiple networks. These multiple networks are network created from Pfam domain structure, co-participation in a protein complex, protein-protein interaction network, genetic interaction network, and network created from cell cycle gene expression measurements. Multiple networks are combined with fixed weights instead of using convex optimization to determine the combination weights due to high time complexity of convex optimization method. This simple combination method will not affect the accuracy performance measures of the three semi-supervised learning methods. Experiment results show that the un-normalized and symmetric normalized graph Laplacian based methods perform slightly better than random walk graph Laplacian based method for integrated network. Moreover, the accuracy performance measures of these three semi-supervised learning methods for integrated network are much better than the best accuracy performance measures of these three methods for the individual network.