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APOGEE 2: multi-layer machine-learning model for the interpretable prediction of mitochondrial missense variants
Open accessPreprint — published as 10.1038/s41467-023-40797-7 · Paper page
Abstract
APOGEE 2 is a mitochondrially-centered ensemble method designed to improve the accuracy of pathogenicity predictions for interpreting missense mitochondrial variants. Built on the joint consensus recommendations by the American College of Medical Genetics and Genomics/Association for Molecular Pathology (ACMG/AMP), APOGEE 2 features an improved machine learning method and a curated training set for enhanced performance metrics. It offers region-wise assessments of genome fragility and mechanistic analyses of specific amino acids that cause perceptible long-range effects on protein structure. With clinical and research use in mind, APOGEE 2 scores and pathogenicity probabilities are precompiled and available in MitImpact. APOGEE 2’s ability to address challenges in interpreting mitochondrial missense variants makes it an essential tool in the field of mitochondrial genetics.
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Metadata
- Authors
- Bianco, S. D., Parca, L., Petrizzelli, F., Biagini, T., Giovannetti, A., Liorni, N., Napoli, A., Carella, M., Procaccio, V., Lott, M., Zhang, S., Vescovi, A. L., Wallace, D. C., Caputo, V., & Mazza, T.
- Affiliations
- Casa Sollievo della Sofferenza (IT)
- Centre National de la Recherche Scientifique (FR)
- Children's Hospital of Philadelphia (US)
- Sapienza University of Rome (IT)
- Corresponding author
- Mazza, Tommaso
- ORCID
- Venue
- Nature Communications
- Publisher
- openRxiv
- Published
- 2023-06-20
- Volume
- 14
- Issue
- 1
- Pages
- 5058-5058
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC10439926
- OpenAlex
- W4385999365
- Cited by
- 44
- References
- 1
- Citations by year
- 2026: 15 · 2025: 15 · 2024: 13 · 2023: 1
- FWCI
- 7.30
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Nature Communications (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- IRIS Research product catalog (Sapienza University of Rome) (submittedVersion, other-oa)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Nvidia
- Ministero della Salute ("5 x 1000"., Ricerca Corrente 2022‐2024, “5×1000”, 2022‐2024, Ricerca Corrente 2020-2022)
- National Institutes of Health (R01OD010944)
- National Institute of Mental Health (R01MH108592)
- National Cancer Institute (R01CA259635)
- National Institute of Neurological Disorders and Stroke (R01NS021328)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (P50HD105354)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Mitochondrial Function and Pathology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Clinical Biochemistry › Metabolism and Genetic Disorders
- Keywords
- mitochondrial DNA, missense mutations, variant pathogenicity prediction, APOGEE-2, ensemble learning, model interpretability, mitochondrial dysfunction, ACMG/AMP guidelines
- MeSH terms
- Machine Learning, Amino Acids, Humans, Mitochondria, Mutation, Mutation, Missense
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