A machine learning-based clinical tool for diagnosing myopathy using multi-cohort microarray expression profiles.

ABSTRACT:

Background

Myopathies are a heterogenous collection of disorders characterized by dysfunction of skeletal muscle. In practice, myopathies are frequently encountered by physicians and precise diagnosis remains a challenge in primary care. Molecular expression profiles show promise for disease diagnosis in various pathologies. We propose a novel machine learning-based clinical tool for predicting muscle disease subtypes using multi-cohort microarray expression data.

Materials and methods

Muscle tissue samples originating from 1260 patients with muscle weakness. Data was curated from 42 independent cohorts with expression profiles in public microarray gene expression repositories, which represent a broad range of patient ages and peripheral muscles. Cohorts were categorized int

SUBMITTER: Tran A 

PROVIDER: S-EPMC7708151 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature