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Essential gene prediction in Drosophila melanogaster using machine learning approaches based on sequence and functional features

Aromolaran, Olufemi and Beder, Thomas and Oswald, M. and Oyelade, O. J. and Adebiyi, E. F. and Koenig, Rainer (2020) Essential gene prediction in Drosophila melanogaster using machine learning approaches based on sequence and functional features. Computational and Structural Biotechnology Journal, 18.

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Genes are termed to be essential if their loss of function compromises viability or results in profound loss of fitness. On the genome scale, these genes can be determined experimentally employing RNAi or knockout screens, but this is very resource intensive. Computational methods for essential gene prediction can overcome this drawback, particularly when intrinsic (e.g. from the protein sequence) as well as extrinsic features (e.g. from transcription profiles) are considered. In this work, we employed machine learning to predict essential genes in Drosophila melanogaster. A total of 27,340 features were generated based on a large variety of different aspects comprising nucleotide and protein sequences, gene networks, protein protein interactions, evolutionary conservation and functional annotations. Employing cross-validation, we obtained an excellent prediction performance. The best model achieved in D. melanogaster a ROCAUC of 0.90, a PR-AUC of 0.30 and a F1 score of 0.34. Our approach considerably outperformed a benchmark method in which only features derived from the protein sequences were used (P < 0.001). Investigating which features contributed to this success, we found all categories of features, most prominently network topological, functional and sequence-based features. To evaluate our approach we performed the same workflow for essential gene prediction in human and achieved an ROC-AUC = 0.97, PR-AUC = 0.73, and F1 = .64. In summary, this study shows that using our well-elaborated assembly of features covering a broad range of intrinsic and extrinsic gene and protein features enabled intelligent systems to predict well the essentiality of genes in an organism.

Item Type: Article
Uncontrolled Keywords: Machine-learning Essential genes Lethal Drosophila Essentiality prediction Homo sapiens
Subjects: Q Science > Q Science (General)
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Engineering, Science and Mathematics > School of Electronics and Computer Science
Depositing User: Mrs Hannah Akinwumi
Date Deposited: 22 Jun 2021 14:04
Last Modified: 22 Jun 2021 14:04

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