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Showing 2 results for “Machine Learning”.

December 2023

Patient Clusters Identified by Machine Learning from a Pooled Analysis of the Clinical Development Programme of Secukinumab in Psoriatic Arthritis, Ankylosing Spondylitis and Psoriatic Arthritis with Axial Manifestations

Clin Exp Rheumatol. 2023 doi: 10.55563/clinexprheumatol/b8co74 Epub ahead of print

Psoriatic arthritis clusters, obtained by machine learning (ML) analysis of pooled data from the FUTURE, MEASURE, and MAXIMISE trials, indicate phenotypical heterogeneity of patients with PsA and axial manifestations and overlapping features across the spondyloarthritis spectrum. Here, Baraliakos, et al. sort to identify distinct clinical clusters, based on patient demographics and baseline clinical indicators, from the secukinumab clinical development programme.

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July 2022

Identification of novel off targets of baricitinib and tofacitinib by machine learning with a focus on thrombosis and viral infection

Sci Rep. 2022 doi: 10.1038/s41598-022-11879-1

Established machine learning approaches, based on ligand similarity, identified previously unknown off-target interactions of baricitinib and tofacitinib, and adds to the evidence that these JAK inhibitors are promiscuous binders, and highlight the potential for repurposing.

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