Severity modeling of propionic acidemia using clinical and laboratory biomarkers.

ABSTRACT:

Purpose

To conduct a proof-of-principle study to identify subtypes of propionic acidemia (PA) and associated biomarkers.

Methods

Data from a clinically diverse PA patient population ( https://clinicaltrials.gov/ct2/show/NCT02890342 ) were used to train and test machine learning models, identify PA-relevant biomarkers, and perform validation analysis using data from liver-transplanted participants. k-Means clustering was used to test for the existence of PA subtypes. Expert knowledge was used to define PA subtypes (mild and severe). Given expert classification, supervised machine learning (support vector machine with a polynomial kernel, svmPoly) performed dimensional reduction to define relevant features of each PA subtype.

Results

Forty participants enrolled in the s

SUBMITTER: Shchelochkov OA 

PROVIDER: S-EPMC8354856 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature