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Authors: Lucia Maddalena 1 ; Ilaria Granata 1 ; Maurizio Giordano 1 ; Mario Manzo 2 ; Mario Guarracino 3 and  Alzheimer’s Disease Neuroimaging Initiative (ADNI) 4

Affiliations: 1 Inst. for High-Performance Computing and Networking, National Research Council,Via P. Castellino, 111, Naples, Italy ; 2 Information Technology Services, University of Naples “L’Orientale”,Via Nuova Marina, 59, Naples, Italy ; 3 University of Cassino and Southern Lazio, Cassino, Italy ; 4

Keyword(s): Data Integration, Alzheimers’ Disease, Omics Imaging, Transcriptomics, Magnetic Resonance Imaging.

Abstract: Early diagnosis of neurodegenerative diseases is essential for the effectiveness of treatments to delay the onset of related symptoms. Our focus is on methods to aid in diagnosing Alzheimer’s disease, the most widespread neurocognitive disorder, that rely on data acquired by non-invasive techniques and that are compatible with the limitations imposed by pandemic situations. Here, we propose integrating multi-modal data consisting of omics (gene expression values extracted by blood samples) and imaging (magnetic resonance images) data, both available for some patients in the Alzheimer’s Disease Neuroimaging Initiative dataset. We show how a suitable integration of omics and imaging data, using well-known machine learning techniques, can lead to better classification results than any of them taken separately, also achieving performance competitive with the state-of-the-art.

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Paper citation in several formats:
Maddalena, L.; Granata, I.; Giordano, M.; Manzo, M.; Guarracino, M. and Alzheimer’s Disease Neuroimaging Initiative (ADNI). (2022). Classifying Alzheimer’s Disease using MRIs and Transcriptomic Data. In Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies - BIOIMAGING, ISBN 978-989-758-552-4; ISSN 2184-4305, pages 70-79. DOI: 10.5220/0010902900003123

@conference{bioimaging22,
author={Lucia Maddalena. and Ilaria Granata. and Maurizio Giordano. and Mario Manzo. and Mario Guarracino. and {Alzheimer’s Disease Neuroimaging Initiative (ADNI)}.},
title={Classifying Alzheimer’s Disease using MRIs and Transcriptomic Data},
booktitle={Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies - BIOIMAGING,},
year={2022},
pages={70-79},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010902900003123},
isbn={978-989-758-552-4},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies - BIOIMAGING,
TI - Classifying Alzheimer’s Disease using MRIs and Transcriptomic Data
SN - 978-989-758-552-4
IS - 2184-4305
AU - Maddalena, L.
AU - Granata, I.
AU - Giordano, M.
AU - Manzo, M.
AU - Guarracino, M.
AU - Alzheimer’s Disease Neuroimaging Initiative (ADNI).
PY - 2022
SP - 70
EP - 79
DO - 10.5220/0010902900003123