Gene Function Prediction with Gene Interaction Networks: A Context Graph Kernel Approach
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.
Cite
@article{arxiv.2204.10473,
title = {Gene Function Prediction with Gene Interaction Networks: A Context Graph Kernel Approach},
author = {Xin Li and Hsinchun Chen and Jiexun Li and Zhu Zhang},
journal= {arXiv preprint arXiv:2204.10473},
year = {2022}
}