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

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.

doi:10.1101/2023.06.18.545476

bioRxiv · 10.1101/2023.06.18.545476

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

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.

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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 2s ability to address challenges in interpreting mitochondrial missense variants makes it an essential tool in the field of mitochondrial genetics.

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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.. 2023-06-20. APOGEE 2: multi-layer machine-learning model for the interpretable prediction of mitochondrial missense variants. https://doi.org/10.1101/2023.06.18.545476

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