NeuroOmics-Net: An interpretable multimodal deep learning framework for Alzheimer's disease diagnosis and progression prediction using neuroimaging, EEG, and genomic data.
- DOI
- 10.1016/j.compbiomed.2026.111879
- Published
- 2026 Sep
- Container
- Computers in biology and medicine
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.compbiomed.2026.111879,
title = {NeuroOmics-Net: An interpretable multimodal deep learning framework for Alzheimer's disease diagnosis and progression prediction using neuroimaging, EEG, and genomic data.},
author = {Kavitha R and Premalatha K},
year = {2026},
journal = {Computers in biology and medicine},
doi = {10.1016/j.compbiomed.2026.111879},
url = {https://doi.org/10.1016/j.compbiomed.2026.111879}
}RIS
TY - JOUR TI - NeuroOmics-Net: An interpretable multimodal deep learning framework for Alzheimer's disease diagnosis and progression prediction using neuroimaging, EEG, and genomic data. AU - Kavitha R AU - Premalatha K PY - 2026 JO - Computers in biology and medicine DO - 10.1016/j.compbiomed.2026.111879 UR - https://doi.org/10.1016/j.compbiomed.2026.111879 ER -
APA
R, K., & K, P. (2026). NeuroOmics-Net: An interpretable multimodal deep learning framework for Alzheimer's disease diagnosis and progression prediction using neuroimaging, EEG, and genomic data.. Computers in biology and medicine. https://doi.org/10.1016/j.compbiomed.2026.111879
Source records
- pubmed · retrieved 2026-09-26T06:35:13.145Z