Gene Expression Prediction Based on Neighbour Connection Neural Network Utilizing Gene Interaction Graphs

Overview

Journal PLoS One

Specialties General Medicine
Science

Date 2023 Feb 6

PMID 36745614

Authors

Xuanyu Li

Xuan Zhang

Wenduo He

Deliang Bu

Sanguo Zhang

Affiliations

Soon will be listed here.

Abstract

Having observed that gene expressions have a correlation, the Library of Integrated Network-based Cell-Signature program selects 1000 landmark genes to predict the remaining gene expression value. Further works have improved the prediction result by using deep learning models. However, these models ignore the latent structure of genes, limiting the accuracy of the experimental results. We therefore propose a novel neural network named Neighbour Connection Neural Network(NCNN) to utilize the gene interaction graph information. Comparing to the popular GCN model, our model incorperates the graph information in a better manner. We validate our model under two different settings and show that our model promotes prediction accuracy comparing to the other models.

Citing Articles

Reinventing gene expression connectivity through regulatory and spatial structural empowerment via principal node aggregation graph neural network.

Yan F, Jiang L, Chen D, Ceccarelli M, Guo Y Nucleic Acids Res. 2024; 52(13):e60.

PMID: 38884259 PMC: 11260459. DOI: 10.1093/nar/gkae514.

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