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Giulia Brigante Unit of Endocrinology, Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy
Unit of Endocrinology, Department of Medical Specialties, Azienda Ospedaliero-Universitaria, Modena, Italy

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Clara Lazzaretti Unit of Endocrinology, Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy

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Elia Paradiso Unit of Endocrinology, Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy

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Federico Nuzzo Unit of Endocrinology, Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy

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Martina Sitti Unit of Endocrinology, Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy

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Frank Tüttelmann Institute of Reproductive Genetics, University of Münster, Münster, Germany

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Gabriele Moretti Department of Biology, University of Pisa, Pisa, Italy

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Roberto Silvestri Department of Biology, University of Pisa, Pisa, Italy

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Federica Gemignani Department of Biology, University of Pisa, Pisa, Italy

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Asta Försti Hopp Children’s Cancer Center (KiTZ), Heidelberg, Germany
Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), German Cancer Consortium (DKTK), Heidelberg, Germany

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Kari Hemminki Biomedical Center, Faculty of Medicine and Biomedical Center in Pilsen, Charles University in Prague, Pilsen, Czech Republic
Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany

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Rossella Elisei Department of Endocrinology, University Hospital, Pisa, Italy

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Cristina Romei Department of Endocrinology, University Hospital, Pisa, Italy

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Eric Adriano Zizzi PolitoBIO Med Lab, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Italy

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Marco Agostino Deriu PolitoBIO Med Lab, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Italy

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Manuela Simoni Unit of Endocrinology, Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy
Unit of Endocrinology, Department of Medical Specialties, Azienda Ospedaliero-Universitaria, Modena, Italy
Center for Genomic Research, University of Modena and Reggio Emilia, Modena, Italy

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Stefano Landi Department of Biology, University of Pisa, Pisa, Italy

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Livio Casarini Unit of Endocrinology, Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy
Center for Genomic Research, University of Modena and Reggio Emilia, Modena, Italy

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, gene–gene interactions are likely too complex to be explained by simple additive or weighted models and alternative methods are under exploration. Machine learning (ML) is increasingly used for predicting individuals’ inherited genomic susceptibility

Article Type:
Research Article
Collections:
Genetics and omics in thyroidology
DOI:
https://doi.org/10.1530/ETJ-22-0058
Volume/Issue:
Volume 11: Issue 5
Open access
Fabyan Esberard de Lima Beltrão Lauro Wanderley University Hospital, Federal University of Paraíba, João Pessoa, Paraíba, Brazil
University Center of João Pessoa – UNIPE, João Pessoa, PB, Brazil

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Daniele Carvalhal de Almeida Beltrão University Center of João Pessoa – UNIPE, João Pessoa, PB, Brazil
Post-Graduation Program in Cognitive Neuroscience and Behavior, Psychology Department of the Center of Human Sciences, Federal University of Paraíba, João Pessoa, Paraíba, Brazil

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Giulia Carvalhal Center for Biological and Health Sciences, Federal University of Campina Grande, Campina Grande, Paraíba, Brazil

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Fabyanna Lethicia de Lima Beltrão Post-Graduate Program in Medicine and Health, Medical School of Medicine, Federal University of Bahia, Salvador, Brazil

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Jocyel de Brito Oliveira Bioregulation Department, Health and Science Institut, Federal University of Bahia, Salvador, Bahia, Brazil

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Hatilla dos Santos Silva Bioregulation Department, Health and Science Institut, Federal University of Bahia, Salvador, Bahia, Brazil

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Helena Mariana Pitangueira Teixeira Bioregulation Department, Health and Science Institut, Federal University of Bahia, Salvador, Bahia, Brazil

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Juliana Lopes Rodrigues Laboratory of Immunopharmacology and Molecular Biology, Health Sciences Institute, Federal University of Bahia, Brazil

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Camila Alexandrina Viana de Figueiredo Laboratory of Immunopharmacology and Molecular Biology, Health Sciences Institute, Federal University of Bahia, Brazil

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Ryan dos Santos Costa Laboratory of Immunopharmacology and Molecular Biology, Health Sciences Institute, Federal University of Bahia, Brazil

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Fabio Hecht The Institute of Biophysics Carlos Chagas Filho, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil

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Giciane Carvalho Vieira Post-Graduation Program in Cognitive Neuroscience and Behavior, Psychology Department of the Center of Human Sciences, Federal University of Paraíba, João Pessoa, Paraíba, Brazil

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Maria da Conceição Rodrigues Gonçalves Lauro Wanderley University Hospital, Federal University of Paraíba, João Pessoa, Paraíba, Brazil

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Antonio C. Bianco Section of Endocrinology and Metabolism, Division of the Biological Sciences, University of Chicago, Chicago, Illinois, USA

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Helton Estrela Ramos Post-Graduate Program in Medicine and Health, Medical School of Medicine, Federal University of Bahia, Salvador, Brazil
Postgraduate Program in Interactive Processes of Organs and Systems, Health & Science Institute, Federal University of Bahia, Salvador, BA, Brazil

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skeletal muscle CT radiomics and machine learning . Thoracic Cancer 2020 11 2650 – 2659 . ( https://doi.org/10.1111/1759-7714.13598 ) 30 Piotrowicz K Ryś M Perera I Gryglewska B Fedyk-Łukasik M Michel JP Wizner B Sydor W Olszanecka A

Article Type:
Research Article
Collections:
Genetics and omics in thyroidology
DOI:
https://doi.org/10.1530/ETJ-24-0068
Volume/Issue:
Volume 13: Issue 4
Open access