EssentialGIN: a new approach for gene essentiality prediction based on graph isomorphism neural networks

2026-06-05

AI Summary

Accurately predicting essential genes remains challenging due to high experimental costs, and traditional network centrality–based approaches suffer from low accuracy and high false-positive rates. This work proposes a deep learning framework based on an improved Graph Isomorphism Network (GIN) that embeds nodes within protein–protein interaction (PPI) networks to preserve topological structure while effectively integrating multi-source biological information, including gene expression, orthology, and subcellular localization. To the best of our knowledge, this is the first application of GIN to essential gene prediction. The proposed method significantly outperforms baseline models—including Node2Vec, MLP, and GAT—on human (*H. sapiens*) data and demonstrates strong generalization performance on *E. coli* and *D. melanogaster*, highlighting its cross-species applicability.

Abstract

Background: Prediction of essential genes (proteins), is a basic and challenging problem but at the same time very costly and time-consuming in wet-lab experiments. Predicting essential genes, only based on computational methods (to introduce wet-lab candidates) using centrality measures are not accurate and result in large number of false positives; therefore, more complex models such as deep learning and also integration of biological information are used in recent research to identify essential genes. Methods: In this work we focus on graph isomorphism networks, in order to embed proteins as a node in PPI network to conserve topological features of PPI network, and also integrate biological data such as gene expression data, gene orthology information and gene subcellular localization information, and introduced a deep architecture for predicting essential genes. Graph isomorphism network architecture is modified in this work for embedding node information. Results: Our experiments proved that the proposed method outperforms baseline centrality-based methods and also machine learning based methods such as Node2Vec, MLP, and also graph attention networks (GAT). Conclusion: In this paper we observed that using graph isomorphism networks that integrate biological data (as node attributes) and preserve network topology can significantly improve the essential gene prediction accuracy. In simpler organisms such as E. coli and D. melanogaster, methods such as multi-layer perceptron using Node2Vec embedding also performs very good, but in H. sapiens the introduced architecture significantly outperforms deep learning and other graph neural network solutions. Keywords: Essential gene prediction, graph neural network, graph isomorphism network, PPI network, node embedding

Problem

• Essential gene prediction
• graph neural network
• PPI network
• node embedding

Innovation

• graph isomorphism network
• gene essentiality prediction
• PPI network
• node embedding
• biological data integration