Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Bianco SD

PROVIDER: S-EPMC10439926 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature

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

Bianco Salvatore Daniele SD Parca Luca L Petrizzelli Francesco F Biagini Tommaso T Giovannetti Agnese A Liorni Niccolò N Napoli Alessandro A Carella Massimo M Procaccio Vincent V Lott Marie T MT Zhang Shiping S Vescovi Angelo Luigi AL Wallace Douglas C DC Caputo Viviana V Mazza Tommaso T

Nature communications 20230819 1

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 b ...[more]

PMID: 37598215

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Project description:OBJECTIVES:Sepsis is among the leading causes of morbidity, mortality, and cost overruns in critically ill patients. Early intervention with antibiotics improves survival in septic patients. However, no clinically validated system exists for real-time prediction of sepsis onset. We aimed to develop and validate an Artificial Intelligence Sepsis Expert algorithm for early prediction of sepsis. DESIGN:Observational cohort study. SETTING:Academic medical center from January 2013 to December 2015. PATIENTS:Over 31,000 admissions to the ICUs at two Emory University hospitals (development cohort), in addition to over 52,000 ICU patients from the publicly available Medical Information Mart for Intensive Care-III ICU database (validation cohort). Patients who met the Third International Consensus Definitions for Sepsis (Sepsis-3) prior to or within 4 hours of their ICU admission were excluded, resulting in roughly 27,000 and 42,000 patients within our development and validation cohorts, respectively. INTERVENTIONS:None. MEASUREMENTS AND MAIN RESULTS:High-resolution vital signs time series and electronic medical record data were extracted. A set of 65 features (variables) were calculated on hourly basis and passed to the Artificial Intelligence Sepsis Expert algorithm to predict onset of sepsis in the proceeding T hours (where T = 12, 8, 6, or 4). Artificial Intelligence Sepsis Expert was used to predict onset of sepsis in the proceeding T hours and to produce a list of the most significant contributing factors. For the 12-, 8-, 6-, and 4-hour ahead prediction of sepsis, Artificial Intelligence Sepsis Expert achieved area under the receiver operating characteristic in the range of 0.83-0.85. Performance of the Artificial Intelligence Sepsis Expert on the development and validation cohorts was indistinguishable. CONCLUSIONS:Using data available in the ICU in real-time, Artificial Intelligence Sepsis Expert can accurately predict the onset of sepsis in an ICU patient 4-12 hours prior to clinical recognition. A prospective study is necessary to determine the clinical utility of the proposed sepsis prediction model.

Dataset Information

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

Publications

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

Similar Datasets

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