Gene Identification in Inflammatory Bowel Disease via a Machine Learning Approach
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INVESTIGACIONMetadatos
Título
Gene Identification in Inflammatory Bowel Disease via a Machine Learning ApproachFecha de publicación
2023-06Editor
MDPICita bibliográfica
Alfonso Perez, G.; Castillo, R. Gene Identification in Inflammatory Bowel Disease via a Machine Learning Approach. Medicina 2023, 59, 1218. https:// doi.org/10.3390/medicina59071218Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
https://www.mdpi.com/1648-9144/59/7/1218Versión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
Inflammatory bowel disease (IBD) is an illness with increasing prevalence, particularly in emerging countries, which can have a substantial impact on the quality of life of the patient. The illness is rather heterog ... [+]
Inflammatory bowel disease (IBD) is an illness with increasing prevalence, particularly in emerging countries, which can have a substantial impact on the quality of life of the patient. The illness is rather heterogeneous with different evolution among patients. A machine learning approach is followed in this paper to identify potential genes that are related to IBD. This is done by following a Monte Carlo simulation approach. In total, 23 different machine learning techniques were tested (in addition to a base level obtained using artificial neural networks). The best model identified 74 genes selected by the algorithm as being potentially involved in IBD. IBD seems to be a polygenic illness, in which environmental factors might play an important role. Following a machine learning approach, it was possible to obtain a classification accuracy of 84.2% differentiating between patients with IBD and control cases in a large cohort of 2490 total cases. The sensitivity and specificity of the model were 82.6% and 84.4%, respectively. It was also possible to distinguish between the two main types of IBD: (1) Crohn’s disease and (2) ulcerative colitis. [-]
Publicado en
Medicina, 2023Entidad financiadora
Ministerio de Ciencia, Innovación y Universidades | Universitat Jaume I
Código del proyecto o subvención
PGC2018-094852-B-C21 | UJI-B2019-43
Derechos de acceso
info:eu-repo/semantics/openAccess
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