EPGAT: Gene Essentiality Prediction With Graph Attention Networks
| Autor: | Mariana Recamonde-Mendoza, Anderson R. Tavares, Joao Schapke |
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| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Graph neural networks Computer science Molecular Networks (q-bio.MN) Machine learning computer.software_genre Machine Learning (cs.LG) Correlation Machine Learning Genetics Humans Quantitative Biology - Molecular Networks Quantitative Biology - Genomics Protein Interaction Maps Gene Genomics (q-bio.GN) Training set Genes Essential business.industry Applied Mathematics Proteins Graph ComputingMethodologies_PATTERNRECOGNITION FOS: Biological sciences Artificial intelligence Neural Networks Computer business computer Algorithms Biotechnology |
| DOI: | 10.48550/arxiv.2007.09671 |
| Popis: | 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. Comment: Published in IEEE/ACM Transactions on Computational Biology and Bioinformatics |
| Databáze: | OpenAIRE |
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