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Novel classification for global gene signature model for predicting severity of systemic sclerosis

Author

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  • Zariel I Johnson
  • Jacqueline D Jones
  • Angana Mukherjee
  • Dianxu Ren
  • Carol Feghali-Bostwick
  • Yvette P Conley
  • Cecelia C Yates

Abstract

Progression of systemic scleroderma (SSc), a chronic connective tissue disease that causes a fibrotic phenotype, is highly heterogeneous amongst patients and difficult to accurately diagnose. To meet this clinical need, we developed a novel three-layer classification model, which analyses gene expression profiles from SSc skin biopsies to diagnose SSc severity. Two SSc skin biopsy microarray datasets were obtained from Gene Expression Omnibus. The skin scores obtained from the original papers were used to further categorize the data into subgroups of low (

Suggested Citation

  • Zariel I Johnson & Jacqueline D Jones & Angana Mukherjee & Dianxu Ren & Carol Feghali-Bostwick & Yvette P Conley & Cecelia C Yates, 2018. "Novel classification for global gene signature model for predicting severity of systemic sclerosis," PLOS ONE, Public Library of Science, vol. 13(6), pages 1-16, June.
    • Handle: RePEc:plo:pone00:0199314
    DOI: 10.1371/journal.pone.0199314
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