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

Journal: Journal of translational medicine
PMID: 33256785

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.

Authors

  • Andrew Tran
    Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.
  • Chris J Walsh
    Keenan Research Center for Biomedical Science, St. Michael's Hospital, Toronto, ON, Canada.
  • Jane Batt
    Keenan Research Center for Biomedical Science, St. Michael's Hospital, Toronto, ON, Canada.
  • Claudia C Dos Santos
    Keenan Research Center for Biomedical Science, St. Michael's Hospital, Toronto, ON, Canada. dossantosC@unityhealth.to.
  • Pingzhao Hu
    c Department of Biochemistry and Medical Genetics , University of Manitoba , Winnipeg , MB , Canada.

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