High-confidence assessment of functional impact of human mitochondrial non-synonymous genome variations by APOGEE

Research output: Contribution to journalArticle

7 Citations (Scopus)

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 languageEnglish
Article numbere1005628
JournalPLoS Computational Biology
Volume13
Issue number6
DOIs
Publication statusPublished - Jun 1 2017

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Test Set
Learning algorithms
anthropogenic activities
Confidence
Learning systems
Amino Acids
Amino acids
Learning Algorithm
Predictors
Machine Learning
Genome
DNA
genome
Genes
Subset
Software
artificial intelligence
Prediction
pathogenicity
Mitochondrial DNA

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Modelling and Simulation
  • Ecology
  • Molecular Biology
  • Genetics
  • Cellular and Molecular Neuroscience
  • Computational Theory and Mathematics

Cite this

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AU - Mazzoccoli, Gianluigi

AU - Biagini, Tommaso

AU - Capocefalo, Daniele

AU - Carella, Massimo

AU - Vescovi, Angelo Luigi

AU - Mazza, Tommaso

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