Classifying Alzheimer’s Disease using MRIs and Transcriptomic Data

Lucia Maddalena, Ilaria Granata, Maurizio Giordano, Mario Rosario Guarracino, Mario Rosario Guarracino, Alzheimer’s Disease Neuroimaging Initiative (ADNI)

2022

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.

Paper Citation

in Harvard Style

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 (BIOSTEC 2022) - Volume 2: BIOIMAGING; ISBN 978-989-758-552-4, SciTePress, pages 70-79. DOI: 10.5220/0010902900003123

in Bibtex Style

@conference{bioimaging22,
author={Lucia Maddalena and Ilaria Granata and Maurizio Giordano and Mario Rosario Manzo and Mario Rosario 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 (BIOSTEC 2022) - Volume 2: BIOIMAGING},
year={2022},
pages={70-79},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010902900003123},
isbn={978-989-758-552-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 2: BIOIMAGING
TI - Classifying Alzheimer’s Disease using MRIs and Transcriptomic Data
SN - 978-989-758-552-4
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
PB - SciTePress