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APOGEE 2: multi-layer machine-learning model for the interpretable prediction of mitochondrial missense variants

bioRxiv (Cold Spring Harbor Laboratory)(2023)10.1101/2023.06.18.545476Source: DataRank Database

APOGEE 2: multi-layer machine-learning model for the interpretable prediction of mitochondrial missense variants is a research paper published in bioRxiv (Cold Spring Harbor Laboratory) (2023). On theSindex it has a DataRank of 0.

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Enrichment:Funding (4 grants)OA: greenTopics (27)IDs (PubMed, OpenAlex)

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Authors (15)

Luca Parca ORCID,Francesco Petrizzelli ORCID,Tommaso Biagini ORCID,Agnese Giovannetti ORCID,Niccolò Liorni ORCID

Live enrichment

PubMed:19767382 OpenAlex:https://openalex.org/W4381431541

National Institutes of Health

Grant: 5R01NS021328-17

MITOCHONDRIAL INBORN ERRORS OF METABOLISM

National Institutes of Health

Grant: 1R01MH108592-01A1

A MITOCHONDRIAL-INTERNEURONAL HYPOTHESIS OF AUTISM

National Institutes of Health

Grant: 1R01CA259635-01A1

Anti-tumor immunity and intestinal microbiota are modulated by mitochondrial DNA

National Institutes of Health

Grant: 3R01OD010944-06S1

Mouse models for mitochondrial disorders caused by mutations in mtDNA

greenLicense: cc-by-nd

repositoryhttps://www.biorxiv.org/content/biorxiv/early/2023/06/20/2023.06.18.545476.full.pdf

publisherhttps://syndication.highwire.org/content/doi/10.1101/2023.06.18.545476

repositoryhttp://dx.doi.org/10.1101/2023.06.18.545476

repositoryhttps://www.nature.com/articles/s41467-023-40797-7

Fields of Study

Genomics and Rare DiseasesMitochondrial Function and PathologyMetabolism and Genetic Disorders0206 medical engineering02 engineering and technology

Keywords

Missense mutationMedical geneticsMachine learningArtificial intelligenceComparative genomicsGenomicsComputational biologyComputer sciencePathogenicityBiologyGenomeGeneticsMutationGeneScienceQMutation, MissenseHumansAmino Acidsgenome; machine learning; mitochondrial DNA; numerical model; pathogenicity; prediction; proteinArticleMitochondria