EPGAT: Gene Essentiality Prediction With Graph Attention Networks

TL;DR

EPGAT employs Graph Attention Networks to accurately predict gene essentiality from PPI networks and multiomics data, outperforming traditional methods and demonstrating robustness with limited data across multiple organisms.

Contribution

The paper introduces EPGAT, a novel GAT-based model that directly learns from graph-structured biological data for essential gene prediction, surpassing existing methods.

Findings

Achieved AUC scores from 0.78 to 0.97 across four organisms.

Outperformed network-based and shallow ML methods.

Showed robustness with limited and imbalanced training data.

Abstract

The identification of essential genes/proteins is a critical step towards a better understanding of human biology and pathology. Computational approaches helped to mitigate experimental constraints by exploring machine learning (ML) methods and the correlation of essentiality with biological information, especially protein-protein interaction (PPI) networks, to predict essential genes. Nonetheless, their performance is still limited, as network-based centralities are not exclusive proxies of essentiality, and traditional ML methods are unable to learn from non-Euclidean domains such as graphs. Given these limitations, we proposed EPGAT, an approach for essentiality prediction based on Graph Attention Networks (GATs), which are attention-based Graph Neural Networks (GNNs) that operate on graph-structured data. Our model directly learns patterns of gene essentiality from PPI networks, integrating additional evidence from multiomics data encoded as node attributes. We benchmarked EPGAT for four organisms, including humans, accurately predicting gene essentiality with AUC score ranging from 0.78 to 0.97. Our model significantly outperformed network-based and shallow ML-based methods and achieved a very competitive performance against the state-of-the-art node2vec embedding method. Notably, EPGAT was the most robust approach in scenarios with limited and imbalanced training data. Thus, the proposed approach offers a powerful and effective way to identify essential genes and proteins.