EPGAT: Gene Essentiality Prediction With Graph Attention Networks

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 graphstructured 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.

ELIMINATOR: Essentiality anaLysIs using MultIsystem Networks And inTeger prOgRamming

“…Despite the recent advent of machine-learning based gene essentiality analyses (Kuang et al, 2020), (Schapke et al, 2020), traditionally, approaches referred to as Constraint-Based Modelling (CBM) leaded the field setting the foundations for the development of different methodologies to predict essential genes (Apaolaza et al, 2017), (Pey et al, 2017), (Tobalina et al, 2016). In essence, CBM integrates omics data in the context of genome-scale metabolic networks resulting in a linear system of inequalities.…”