Differential diagnosis of systemic lupus erythematosus and Sjögren’s syndrome using machine learning and multi-omics data
Metadatos
Mostrar el registro completo del ítemEditorial
Elsevier
Materia
Machine learning Modeling and prediction Bioinformatics Clustering Classification and association rules Health
Fecha
2022-11-28Referencia bibliográfica
Jordi Martorell-Marugán... [et al.], Differential diagnosis of systemic lupus erythematosus and Sjögren's syndrome using machine learning and multi-omics data, Computers in Biology and Medicine, Volume 152, 2023, 106373, ISSN 0010-4825, [https://doi.org/10.1016/j.compbiomed.2022.106373]
Patrocinador
MCIN/AEI PID2020-119032RB-I00; FEDER/Junta de Andalucia-Consejeria de Transformacion Economica, Industria, Conocimiento y Universidades P20_00335 B-CTS-40-UGR20; EU/EFPIA Innovative Medicines Initiative Joint Undertaking PRECISESADS 115565; Gustaf den Ve:80-ars fond for MEAR; European Union - NextGenerationEU; Ministerio de Universidades (Spain's Government); University of Granada; European Molecular Biology Organization (EMBO) ASTF 8692Resumen
Systemic lupus erythematosus and primary Sjogren’s syndrome are complex systemic autoimmune diseases that
are often misdiagnosed. In this article, we demonstrate the potential of machine learning to perform differential
diagnosis of these similar pathologies using gene expression and methylation data from 651 individuals.
Furthermore, we analyzed the impact of the heterogeneity of these diseases on the performance of the predictive
models, discovering that patients assigned to a specific molecular cluster are misclassified more often and affect
to the overall performance of the predictive models. In addition, we found that the samples characterized by a
high interferon activity are the ones predicted with more accuracy, followed by the samples with high inflammatory
activity. Finally, we identified a group of biomarkers that improve the predictions compared to using the
whole data and we validated them with external studies from other tissues and technological platforms.