Detalle de: Colonic MicroRNA Profiles, Identified by a Deep Learning Algorithm, That Predict Responses to Therapy of Patients With Acute Severe Ulcerative Colitis
| Title | Colonic MicroRNA Profiles, Identified by a Deep Learning Algorithm, That Predict Responses to Therapy of Patients With Acute Severe Ulcerative Colitis |
| Abstract |
We developed, DEEPCOL, an algorithm that identifies patients with ASUC who respond vs do not respond to first- and second-line treatments, based on microRNA expression profiles in colon tissues. |
| Apa Reference | Morilla, I., Uzzan, M., Laharie, D., Cazals-Hatem, D., Denost, Q., Daniel, F., … Treton, X. (2019). Colonic MicroRNA profiles, identified by a deep learning algorithm, that predict responses to therapy of patients with acute severe ulcerative colitis. Clinical Gastroenterology and Hepatology: The Official Clinical Practice Journal of the American Gastroenterological Association, 17(5), 905–913. doi:10.1016/j.cgh.2018.08.068 |
| Doi | 10.1016/j.cgh.2018.08.068 |
| Publication Date | Apr 2019 |
| Publication Type | Article |
| Researcher | Ian Morilla |
| Ontology | Artificial Intelligence, Expression Profiling, Regulatory biology, Deep Learning in Biology |
| Research Groups | Machine Learning in Multi-Omics (MLiMO) |
| Tools |