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

• URL: http://arxiv.org/abs/2204.10473v1
• Date: Fri, 22 Apr 2022 02:54:01 GMT
• Title: Gene Function Prediction with Gene Interaction Networks: A Context Graph Kernel Approach
• Authors: Xin Li, Hsinchun Chen, Jiexun Li, Zhu Zhang
• Abstract summary: 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.
• Score: 24.234645183601998
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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