Differential diagnosis of systemic lupus erythematosus and Sjögren’s syndrome using machine learning and multi-omics data Martorell Marugán, Jordi Alarcón Riquelme, Marta Eugenia Carmona Sáez, Pedro Machine learning Modeling and prediction Bioinformatics Clustering Classification and association rules Health 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. 2023-03-16T08:45:22Z 2023-03-16T08:45:22Z 2022-11-28 journal article 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] https://hdl.handle.net/10481/80618 10.1016/j.compbiomed.2022.106373 eng http://creativecommons.org/licenses/by/4.0/ http://creativecommons.org/licenses/by/4.0/ open access Atribución 4.0 Internacional Atribución 4.0 Internacional Elsevier