Abstract
24,189 are all the possible non-synonymous amino acid changes potentially affecting the human mitochondrial DNA. Only a tiny subset was functionally evaluated with certainty so far, while the pathogenicity of the vast majority was only assessed in-silico by software predictors. Since these tools proved to be rather incongruent, we have designed and implemented APOGEE, a machine-learning algorithm that outperforms all existing prediction methods in estimating the harmfulness of mitochondrial non-synonymous genome variations. We provide a detailed description of the underlying algorithm, of the selected and manually curated training and test sets of variants, as well as of its classification ability.
Original language | English |
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Article number | e1005628 |
Journal | PLoS Computational Biology |
Volume | 13 |
Issue number | 6 |
DOIs | |
Publication status | Published - Jun 1 2017 |
ASJC Scopus subject areas
- Ecology, Evolution, Behavior and Systematics
- Modelling and Simulation
- Ecology
- Molecular Biology
- Genetics
- Cellular and Molecular Neuroscience
- Computational Theory and Mathematics