Gene Function Prediction with Gene Interaction Networks: A Context Graph Kernel Approach

TL;DR

This paper introduces a novel kernel-based method utilizing context graphs derived from gene interaction networks to improve gene function prediction, demonstrating superiority over traditional linkage-assumption methods in biological datasets.

Contribution

The study proposes a new context graph kernel that captures indirect gene interactions for more accurate gene function inference, advancing beyond linkage-based assumptions.

Findings

Outperforms linkage-assumption-based methods

Utilizes indirect gene interactions effectively

Shows empirical superiority on p53-related genes

Abstract

Predicting gene functions is a challenge for biologists in the post genomic era. Interactions among genes and their products compose networks that can be used to infer gene functions. Most previous studies adopt a linkage assumption, i.e., they assume that gene interactions indicate functional similarities between connected genes. In this study, we propose to use a gene's context graph, i.e., the gene interaction network associated with the focal gene, to infer its functions. In a kernel-based machine-learning framework, we design a context graph kernel to capture the information in context graphs. Our experimental study on a testbed of p53-related genes demonstrates the advantage of using indirect gene interactions and shows the empirical superiority of the proposed approach over linkage-assumption-based methods, such as the algorithm to minimize inconsistent connected genes and diffusion kernels.