Novel approach to early prediction of alzheimer's disease progression using integrated deep regulatory genetic neural network and optimized deep belief networks.

Overview

Authors: S. Roobini¹, M.S Kavitha¹, S. Karthik¹

Department of Computer Science and Engineering, SNS College of Technology,Coimbatore, Tamil Nadu 641 035 India

Institutions: Anna University, Chennai (India)

Journal: Scientific reports, volume 16, issue 1, article 22966

Dates: received 10 November 2025; accepted 22 May 2026; published online 10 July 2026

Type: Research article · Language: English

License: CC BY-NC-ND

Identifiers: DOI 10.1038/s41598-026-55178-5 · PMID 42431913 · PMCID PMC13392091 · OpenAlex W7167896913

Open access: gold, a free copy (OpenAlex)

Status: data only

Categories: computational modeling (no new data) (modality), human (organism), Alzheimer's / dementia (population), methods / tools (subfield)

Methods: Machine learning

Keywords: Alzheimer’s disease, Computational modelling, Convolutional neural networks, Data models, Deep learning, Disease accuracy, Disease progression, Predictive models, Computational biology and bioinformatics, Neurology, Neuroscience

MeSH: Alzheimer Disease*, Deep Learning*, Gene Regulatory Networks*, Neural Networks, Computer*, Disease Progression, Humans, Prediction Algorithms, Predictive Learning Models (* major topic)

Topic: Artificial Intelligence in Healthcare (Health Information Management, Health Professions), according to OpenAlex

Citations: not cited yet (Europe PMC); 36 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Code

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Data

Datasets cited

geo:GSE5281, at NCBI GEO; found in “Data availability”

Data availability statement

The paper has a data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

it points to a dataset: NCBI GEO GSE5281

Read it in the paper: doi.org/10.1038/s41598-026-55178-5.

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 3 authors, 11 keywords, 8 MeSH terms, 36 references.

Cite

This paper

Roobini, S., Kavitha, M., & Karthik, S. (2026). Novel approach to early prediction of alzheimer's disease progression using integrated deep regulatory genetic neural network and optimized deep belief networks. Scientific reports, 16(1), 22966. https://doi.org/10.1038/s41598-026-55178-5

BibTeX

@article{roobini2026novel, author = {Roobini, S. and Kavitha, M.S and Karthik, S.}, title = {{Novel approach to early prediction of alzheimer's disease progression using integrated deep regulatory genetic neural network and optimized deep belief networks}}, journal = {Scientific reports}, year = {2026}, month = jul, volume = {16}, number = {1}, pages = {22966}, publisher = {Nature Publishing Group}, issn = {2045-2322}, doi = {10.1038/s41598-026-55178-5}, url = {https://doi.org/10.1038/s41598-026-55178-5}, pmid = {42431913}, pmcid = {PMC13392091} }

RIS

TY - JOUR AU - Roobini, S. AU - Kavitha, M.S AU - Karthik, S. TI - Novel approach to early prediction of alzheimer's disease progression using integrated deep regulatory genetic neural network and optimized deep belief networks T2 - Scientific reports J2 - Sci Rep PY - 2026 DA - 2026/07/10 VL - 16 IS - 1 SP - 22966 SN - 2045-2322 PB - Nature Publishing Group DO - 10.1038/s41598-026-55178-5 UR - https://doi.org/10.1038/s41598-026-55178-5 LA - en ER -

CSL-JSON

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