Asset Details
Do you wish to reserve the book?
APOGEE 2: multi-layer machine-learning model for the interpretable prediction of mitochondrial missense variants
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
APOGEE 2: multi-layer machine-learning model for the interpretable prediction of mitochondrial missense variants
Do you wish to request the book?
APOGEE 2: multi-layer machine-learning model for the interpretable prediction of mitochondrial missense variants
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
APOGEE 2: multi-layer machine-learning model for the interpretable prediction of mitochondrial missense variants
2023
Overview
Mitochondrial dysfunction has pleiotropic effects and is frequently caused by mitochondrial DNA mutations. However, factors such as significant variability in clinical manifestations make interpreting the pathogenicity of variants in the mitochondrial genome challenging. Here, we present APOGEE 2, 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, 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.
APOGEE 2 is a machine-learning tool for assessing the fragility of the mitochondrial genome, evaluating genetic variant pathogenicity and ultimately enhancing our understanding of the clinical heterogeneity of mitochondrial genetic diseases.
