Model selection for metabolomics: predicting diagnosis of coronary artery disease using automated machine learning.

ABSTRACT:

Motivation

Selecting the optimal machine learning (ML) model for a given dataset is often challenging. Automated ML (AutoML) has emerged as a powerful tool for enabling the automatic selection of ML methods and parameter settings for the prediction of biomedical endpoints. Here, we apply the tree-based pipeline optimization tool (TPOT) to predict angiographic diagnoses of coronary artery disease (CAD). With TPOT, ML models are represented as expression trees and optimal pipelines discovered using a stochastic search method called genetic programing. We provide some guidelines for TPOT-based ML pipeline selection and optimization-based on various clinical phenotypes and high-throughput metabolic profiles in the Angiography and Genes Study (ANGES).

Results

We analyzed nuclear

SUBMITTER: Orlenko A 

PROVIDER: S-EPMC7703753 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature