machine learning

A Framework for Efficient N-Way Interaction Testing in Case/Control Studies With Categorical Data

Author(s): Aristos Aristodimou, Athos Antoniades, Efthimios Dardiotis, Eleni M. Loizidou, George M. Spyrou, Christina Votsi, Kyproula Christodoulou, Marios Pantzaris, Nikolaos Grigoriadis, Georgios M. Hadjigeorgiou, Theodoros Kyriakides, Constantinos S. Pattichis
A Framework for Efficient N-Way Interaction Testing in Case/Control Studies With Categorical Data 150 150 IEEE Open Journal of Engineering in Medicine and Biology (OJEMB)

Most common diseases are influenced by multiple gene interactions and interactions with the environment. Performing an exhaustive search to identify such interactions is computationally expensive and needs to address the…

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SARS-CoV-2 Detection from Voice

Author(s): Gadi Pinkas, Yarden Karny, Aviad Malachi, Galia Barkai, Gideon Bachar, Vered Aharonson
SARS-CoV-2 Detection from Voice 150 150 IEEE Open Journal of Engineering in Medicine and Biology (OJEMB)

Abstract: Automated voice-based detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) could facilitate the screening for COVID19. A dataset of cellular phone recordings from 88 subjects was recently collected.…

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Accelerometry-Based Digital Gait Characteristics for Classification of Parkinson’s Disease: What Counts?

Author(s): Silvia Del Din, Cameron Kirk, Lynn Rochester, Christopher Buckley, Maria Encarna Mico-Amigo, Claudia Mazza, Michael Dunne-Willows, Jian Quing Shi, Rana Zia Ur Rehman, Lisa Alcock
Accelerometry-Based Digital Gait Characteristics for Classification of Parkinson’s Disease: What Counts? 800 545 IEEE Open Journal of Engineering in Medicine and Biology (OJEMB)

Gait may be a useful biomarker that can be objectively measured with wearable technology to classify Parkinson’s disease (PD). This study aims to: (i) comprehensively quantify a battery of commonly…

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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

Author(s): Clio Mavragani, Konstantina D. Kourou, Costas Papaloukas, Michalis Voulgarelis, Andreas Goules, Dimitrios I. Fotiadis, Themis Exarchos, Andrianos Nezos, Eleni I. Georga, Vasileios C. Pezoulas, Athanasios G. Tzioufas, Haralampos M. Moutsopoulos
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 800 534 IEEE Open Journal of Engineering in Medicine and Biology (OJEMB)

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…

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