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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IEEE
2020-01-01
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| Series: | IEEE Open Journal of Engineering in Medicine and Biology |
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| Online Access: | https://ieeexplore.ieee.org/document/8954752/ |
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| author | 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 |
| author_facet | 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 |
| author_sort | Konstantina D. Kourou |
| collection | DOAJ |
| description | 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. <italic>Objective:</italic> The current study aims to explore whether genetic susceptibility profiles of SS patients along with known clinical, serological and histological risk factors enhance the accuracy of predicting lymphoma development in this patient population. <italic>Methods:</italic> The potential predicting role of both genetic variants, clinical and laboratory risk factors were investigated through a Machine Learning-based (ML) framework which encapsulates ensemble classifiers. <italic>Results</italic>: Ensemble methods empower the classification accuracy with approaches which are sensitive to minor perturbations in the training phase. The evaluation of the proposed methodology based on a 10-fold stratified cross validation procedure yielded considerable results in terms of balanced accuracy (GB: 0.7780 ± 0.1514, RF Gini: 0.7626 ± 0.1787, RF Entropy: 0.7590 ± 0.1837). <italic>Conclusions:</italic> The initial clinical, serological, histological and genetic findings at an early diagnosis have been exploited in an attempt to establish predictive tools in clinical practice and further enhance our understanding towards lymphoma development in SS. |
| format | Article |
| id | doaj-art-2d633b63df074461851f80840e7a0cf9 |
| institution | Kabale University |
| issn | 2644-1276 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Engineering in Medicine and Biology |
| spelling | doaj-art-2d633b63df074461851f80840e7a0cf92025-08-20T03:33:14ZengIEEEIEEE Open Journal of Engineering in Medicine and Biology2644-12762020-01-011495610.1109/OJEMB.2020.29651918954752Predicting 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 PatientsKonstantina D. Kourou0https://orcid.org/0000-0003-4310-2739Vasileios C. Pezoulas1https://orcid.org/0000-0002-1872-693XEleni I. Georga2https://orcid.org/0000-0002-3607-0727Themis Exarchos3Costas Papaloukas4https://orcid.org/0000-0002-6736-5536Michalis Voulgarelis5Andreas Goules6Andrianos Nezos7Athanasios G. Tzioufas8Earalampos M. Moutsopoulos9Clio Mavragani10Dimitrios I. Fotiadis11https://orcid.org/0000-0002-5987-9350Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, The University of Ioannina, Ioannina, GreeceUnit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, The University of Ioannina, Ioannina, GreeceUnit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, The University of Ioannina, Ioannina, GreeceUnit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, The University of Ioannina, Ioannina, GreeceUnit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, The University of Ioannina, Ioannina, GreeceDepartment of Biomedical Research, Foundation for Research and Technology-Hellas, Institute of Molecular Biology and Biotechnology, Ioannina, GreeceDepartment of Biomedical Research, Foundation for Research and Technology-Hellas, Institute of Molecular Biology and Biotechnology, Ioannina, GreeceDepartment of Physiology, School of Medicine, National and Kapodistrian University of Athens, Athens, GreeceDepartment of Biomedical Research, Foundation for Research and Technology-Hellas, Institute of Molecular Biology and Biotechnology, Ioannina, GreeceAcademy of Athens, Athens, GreeceDepartment of Pathophysiology, School of Medicine, National and Kapodistrian University of Athens, Athens, GreeceUnit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, The University of Ioannina, Ioannina, GreeceLymphoma 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. <italic>Objective:</italic> The current study aims to explore whether genetic susceptibility profiles of SS patients along with known clinical, serological and histological risk factors enhance the accuracy of predicting lymphoma development in this patient population. <italic>Methods:</italic> The potential predicting role of both genetic variants, clinical and laboratory risk factors were investigated through a Machine Learning-based (ML) framework which encapsulates ensemble classifiers. <italic>Results</italic>: Ensemble methods empower the classification accuracy with approaches which are sensitive to minor perturbations in the training phase. The evaluation of the proposed methodology based on a 10-fold stratified cross validation procedure yielded considerable results in terms of balanced accuracy (GB: 0.7780 ± 0.1514, RF Gini: 0.7626 ± 0.1787, RF Entropy: 0.7590 ± 0.1837). <italic>Conclusions:</italic> The initial clinical, serological, histological and genetic findings at an early diagnosis have been exploited in an attempt to establish predictive tools in clinical practice and further enhance our understanding towards lymphoma development in SS.https://ieeexplore.ieee.org/document/8954752/Ensemble methodsgenetic variantslymphoma predictionmachine learningSjögren's Syndrome |
| spellingShingle | 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 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 IEEE Open Journal of Engineering in Medicine and Biology Ensemble methods genetic variants lymphoma prediction machine learning Sjögren's Syndrome |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | predicting lymphoma development by exploiting genetic variants and clinical findings in a machine learning based methodology with ensemble classifiers in a cohort of sj x00f6 gren x0027 s syndrome patients |
| topic | Ensemble methods genetic variants lymphoma prediction machine learning Sjögren's Syndrome |
| url | https://ieeexplore.ieee.org/document/8954752/ |
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