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Expert Review of Clinical Immunology|July 28, 2011
Capturing the heterogeneity in systemic sclerosis with genome-wide expression profilingJennifer L Sargent, Michael L WhitfieldCurrent Opinion in Rheumatology|October 6, 2007
Understanding systemic sclerosis through gene expression profilingSarah A Pendergrass, Michael L Whitfield, Humphrey GardnerAnnals of the Rheumatic Diseases|October 8, 2020
Machine learning integration of scleroderma histology and gene expression identifies fibroblast polarisation as a hallmark of clinical severity and improvementKimberly Showalter, Robert Spiera, Cynthia Magro, et al.Seminars in Immunopathology|July 31, 2015
Gene expression profiling offers insights into the role of innate immune signaling in SScMichael E Johnson, Patricia A Pioli, Michael L WhitfieldAnnual Review of Pathology|November 25, 2010
The pathogenesis of systemic sclerosisTamiko R Katsumoto, Michael L Whitfield, M Kari ConnollyAnnals of the Rheumatic Diseases|September 16, 2020
Machine learning predicts stem cell transplant response in severe sclerodermaJennifer M Franks, Viktor Martyanov, Yue Wang, et al.Plos Computational Biology|July 23, 2013
Transcription factor binding profiles reveal cyclic expression of human protein-coding genes and non-coding RNAsChao Cheng, Matthew Ung, Gavin D Grant, et al.Rheumatology (Oxford, England)|June 25, 2022
A genomic meta-analysis of clinical variables and their association with intrinsic molecular subsets in systemic sclerosisJennifer M Franks, Diana M Toledo, Viktor Martyanov, et al.European Journal of Rheumatology|November 9, 2020
Molecular "omic" signatures in systemic sclerosisBhaven K Mehta, Monica E Espinoza, Monique Hinchcliff, et al.The Journal of Investigative Dermatology|December 25, 2016
A Functional Genomic Meta-Analysis of Clinical Trials in Systemic Sclerosis: Toward Precision Medicine and Combination TherapyJaclyn N Taroni, Viktor Martyanov, J Matthew Mahoney, et al.Pageof 12