Machine learning algorithms reveal unique gene expression profiles in muscle biopsies from patients with different types of myositis.

ABSTRACT:

Objectives

Myositis is a heterogeneous family of diseases that includes dermatomyositis (DM), antisynthetase syndrome (AS), immune-mediated necrotising myopathy (IMNM), inclusion body myositis (IBM), polymyositis and overlap myositis. Additional subtypes of myositis can be defined by the presence of myositis-specific autoantibodies (MSAs). The purpose of this study was to define unique gene expression profiles in muscle biopsies from patients with MSA-positive DM, AS and IMNM as well as IBM.

Methods

RNA-seq was performed on muscle biopsies from 119 myositis patients with IBM or defined MSAs and 20 controls. Machine learning algorithms were trained on transcriptomic data and recursive feature elimination was used to determine which genes were most useful for classifying muscl

SUBMITTER: Pinal-Fernandez I 

PROVIDER: S-EPMC10461844 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature