Deep belief network-based approach for detecting Alzheimer's disease using the multi-omics data
Open accessAbstract
Alzheimer's disease (AD) is the most uncertain form of Dementia in terms of finding out the mechanism. AD does not have a vital genetic factor to relate to. There were no reliable techniques and methods to identify the genetic risk factors associated with AD in the past. Most of the data available were from the brain images. However, recently, there have been drastic advancements in the high-throughput techniques in bioinformatics. It has led to focused researches in discovering the AD causing genetic risk factors. Recent analysis has resulted in considerable prefrontal cortex data with which classification and prediction models can be developed for AD. We have developed a Deep Belief Network-based prediction model using the DNA Methylation and Gene Expression Microarray Data, with High Dimension Low Sample Size (HDLSS) issues. To overcome the HDLSS challenge, we performed a two-layer feature selection considering the biological aspects of the features as well. In the two-layered feature selection approach, first the differentially expressed genes and differentially methylated positions are identified, then both the datasets are combined using Jaccard similarity measure. As the second step, an ensemble-based feature selection approach is implemented to further narrow down the gene selection. The results show that the proposed feature selection technique outperforms the existing commonly used feature selection techniques, such as Support Vector Machine Recursive Feature Elimination (SVM-RFE), and Correlation-based Feature Selection (CBS). Furthermore, the Deep Belief Network-based prediction model performs better than the widely used Machine Learning models. Also, the multi-omics dataset shows promising results compared to the single omics.
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DBNs have proven effective in addressing the high-dimensional, low-sample-size challenge inherent to multi-omics datasets, demonstrating superior performance in feature selection and classification compared to conventional methods such as SVM-RFE.
“DBNs have also proven effective in addressing the high-dimensional, low-sample-size challenge inherent to multi-omics datasets, demonstrating superior performance in feature selection and classification compared to conventional methods such as SVM-RFE237.”
From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [237]
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Experimental — accuracy not yet measuredDBNs integrate gene expression and methylation features to improve classification accuracy, while E-VAEs reveal interpretable genomic-transcriptomic interactions linked to AD pathways.
“Deep Belief Networks (DBNs) integrate gene expression and methylation features to improve classification accuracy, while Explainable Variational Autoencoders (E-VAEs) reveal interpretable genomic-transcriptomic interactions linked to AD pathways237,285.”
From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [237]
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- Authors
- Mahendran, N., & Vincent P M, D. R.
- Affiliations
- Vellore Institute of Technology University (IN)
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India
- Corresponding author
- Mahendran, Nivedhitha
- Venue
- Computational and Structural Biotechnology Journal
- Published
- 2023-01
- Volume
- 21
- Pages
- 1651-1660
- ISSN
- 2001-0370 (ISSN-L)
- PMC
- PMC9978469
- OpenAlex
- W4320487111
- Cited by
- 31
- References
- 43
- Citations by year
- 2026: 7 · 2025: 9 · 2024: 12 · 2023: 3
- FWCI
- 2.76
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Computational and Structural Biotechnology Journal (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Keywords
- Alzheimer's disease, deep belief network, multi-omics data, DNA methylation, gene expression microarrays, high-dimensional small-sample data, feature selection, Jaccard similarity, ensemble feature selection, differentially methylated positions, differentially expressed genes, prefrontal cortex, Alzheimer's disease prediction
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