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Response to: ‘Correspondence on ‘Machine learning algorithms reveal unique gene expression profiles in muscle biopsies from patients with different types of myositis’’ by Takanashi et al
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Authors

  • Iago Pinal-Fernandez Muscle Disease Unit, National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health, Bethesda, Maryland, USA Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA Faculty of Health Sciences and Faculty of Computer Science, Multimedia and Telecommunications, Universitat Oberta de Catalunya, Barcelona, Barcelona, Spain PubMed articlesGoogle scholar articles
  • Maria Casal-Dominguez Muscle Disease Unit, National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health, Bethesda, Maryland, USA Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA PubMed articlesGoogle scholar articles
  • Jose Cesar Milisenda Internal Medicine, Hospital Clinic de Barcelona, Barcelona, Catalunya, Spain PubMed articlesGoogle scholar articles
  • Andrew Lee Mammen Muscle Disease Unit, National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health, Bethesda, Maryland, USA Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA PubMed articlesGoogle scholar articles
  1. Correspondence to Dr Iago Pinal-Fernandez, Muscle Disease Unit, National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health, Bethesda, Maryland, USA; iago.pinalfernandez{at}nih.gov; Dr Andrew Lee Mammen, Muscle Disease Unit, National Institute of Arthritis and Musculoskeletal and Skin Diseases, National Institutes of Health, Bethesda, Marylan, USA; andrew.mammen{at}nih.gov
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Citation

Pinal-Fernandez I, Casal-Dominguez M, Milisenda JC, et al
Response to: ‘Correspondence on ‘Machine learning algorithms reveal unique gene expression profiles in muscle biopsies from patients with different types of myositis’’ by Takanashi et al

Publication history

  • Received December 22, 2020
  • Accepted December 23, 2020
  • First published January 13, 2021.
Online issue publication 
February 10, 2023

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