Differentiation between Descending Thoracic Aortic Diseases using Machine Learning and Plasma Proteomic Signatures.

ABSTRACT:

Background

Descending thoracic aortic aneurysms and dissections can go undetected until severe and catastrophic, and few clinical indices exist to screen for aneurysms or predict risk of dissection.

Methods

This study generated a plasma proteomic dataset from 75 patients with descending type B dissection (Type B) and 62 patients with descending thoracic aortic aneurysm (DTAA). Standard statistical approaches were compared to supervised machine learning (ML) algorithms to distinguish Type B from DTAA cases. Quantitatively similar proteins were clustered based on linkage distance from hierarchical clustering and ML models were trained with uncorrelated protein lists across various linkage distances with hyperparameter optimization using 5-fold cross validation. Permutation imp

SUBMITTER: Momenzadeh A 

PROVIDER: S-EPMC10168345 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature