Predicting Lymphoma Development by Exploiting Genetic Variants and Clinical Findings in a Machine Learning-Based Methodology With Ensemble Classifiers in a Cohort of Sjögren's Syndrome Patients.
Ensemble methods
Sjögren's Syndrome
genetic variants
lymphoma prediction
machine learning
Journal
IEEE open journal of engineering in medicine and biology
ISSN: 2644-1276
Titre abrégé: IEEE Open J Eng Med Biol
Pays: United States
ID NLM: 101766631
Informations de publication
Date de publication:
2020
2020
Historique:
received:
06
11
2019
revised:
20
12
2019
accepted:
20
12
2019
entrez:
11
4
2022
pubmed:
14
2
2020
medline:
14
2
2020
Statut:
epublish
Résumé
Lymphoma development constitutes one of the most serious clinico-pathological manifestations of patients with Sjögren's Syndrome (SS). Over the last decades the risk for lymphomagenesis in SS patients has been studied aiming to identify novel biomarkers and risk factors predicting lymphoma development in this patient population.
Identifiants
pubmed: 35402956
doi: 10.1109/OJEMB.2020.2965191
pmc: PMC8979630
doi:
Types de publication
Journal Article
Langues
eng
Pagination
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