Multiple graph regularized protein domain ranking

TL;DR

This paper introduces MultiG-Rank, a novel method that combines multiple graphs to improve protein domain ranking by capturing the global structure more effectively, outperforming existing single-graph methods.

Contribution

The paper proposes a multiple graph regularized ranking algorithm that automatically learns graph weights, enhancing robustness and performance in protein domain ranking tasks.

Findings

MultiG-Rank outperforms single graph methods.

Combining multiple graphs improves ranking accuracy.

Automatic graph weight learning enhances robustness.

Abstract

Background Protein domain ranking is a fundamental task in structural biology. Most protein domain ranking methods rely on the pairwise comparison of protein domains while neglecting the global manifold structure of the protein domain database. Recently, graph regularized ranking that exploits the global structure of the graph defined by the pairwise similarities has been proposed. However, the existing graph regularized ranking methods are very sensitive to the choice of the graph model and parameters, and this remains a difficult problem for most of the protein domain ranking methods. Results To tackle this problem, we have developed the Multiple Graph regularized Ranking algorithm, MultiG- Rank. Instead of using a single graph to regularize the ranking scores, MultiG-Rank approximates the intrinsic manifold of protein domain distribution by combining multiple initial graphs for the regularization. Graph weights are learned with ranking scores jointly and automatically, by alternately minimizing an ob- jective function in an iterative algorithm. Experimental results on a subset of the ASTRAL SCOP protein domain database demonstrate that MultiG-Rank achieves a better ranking performance than single graph regularized ranking methods and pairwise similarity based ranking methods. Conclusion The problem of graph model and parameter selection in graph regularized protein domain ranking can be solved effectively by combining multiple graphs. This aspect of generalization introduces a new frontier in applying multiple graphs to solving protein domain ranking applications.