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

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

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 predict...

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Main Authors: Konstantina D. Kourou, Vasileios C. Pezoulas, Eleni I. Georga, Themis Exarchos, Costas Papaloukas, Michalis Voulgarelis, Andreas Goules, Andrianos Nezos, Athanasios G. Tzioufas, Earalampos M. Moutsopoulos, Clio Mavragani, Dimitrios I. Fotiadis
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Open Journal of Engineering in Medicine and Biology
Subjects:
Ensemble methods
genetic variants
lymphoma prediction
machine learning
Sjögren's Syndrome
Online Access:https://ieeexplore.ieee.org/document/8954752/
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