ArticleComputational and structural biotechnology journal2023
Artificial intelligence-driven meta-analysis of brain gene expression identifies novel gene candidates and a role for mitochondria in Alzheimer's disease.
Article in Computational and structural biotechnology journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 20 citations in OpenAlex.
- Integrative gene co-expression network analysis reveals protein-coding and LncRNA genes associated with Alzheimer's disease pathology.Scientific reports · 2025Article
- Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions.Acta pharmaceutica Sinica. B · 2025Review
- Proteome profiling of cerebrospinal fluid using machine learning shows a unique protein signature associated with APOE4 genotype.Aging cell · 2025Article
- A Comprehensive Review on Deep Learning Techniques in Alzheimer's Disease Diagnosis.Current topics in medicinal chemistry · 2025Review
- Improving machine learning detection of Alzheimer disease using enhanced manta ray gene selection of Alzheimer gene expression datasets.PeerJ. Computer science · 2025Article
- Cerebellum in Alzheimer's disease and other neurodegenerative diseases: an emerging research frontier.MedComm · 2024Review
- Proteome profiling of cerebrospinal fluid using machine learning shows a unique protein signature associated with APOE4 genotype.bioRxiv : the preprint server for biology · 2024Article
- Investigating the Potential Impact of Air Pollution on Alzheimer's Disease and the Utility of Multidimensional Imaging for Early Detection.ACS omega · 2024Review
- Mitochondria in Alzheimer's Disease Pathogenesis.Life (Basel, Switzerland) · 2024Review
- Blood-Based Transcriptomic Biomarkers Are Predictive of Neurodegeneration Rather Than Alzheimer's Disease.International journal of molecular sciences · 2023Article
- Multifaceted roles of mitochondrial dysfunction in diseases: from powerhouses to saboteurs.Archives of pharmacal research · 2023Review
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Alzheimer's disease (AD) is the most common form of dementia. There is no treatment and AD models have focused on a small subset of genes identified in familial AD. Microarray studies have identified thousands of dysregulated genes in the brains of patients with AD yet identifying the best gene candidates to both model and treat AD remains a challenge. We performed a meta-analysis of microarray data from the frontal cortex (n = 697) and cerebellum (n = 230) of AD patients and healthy controls. A two-stage artificial intelligence approach, with both unsupervised and supervised machine learning, combined with a functional network analysis was used to identify functionally connected and biologically relevant novel gene candidates in AD. We found that in the frontal cortex, genes involved in mitochondrial energy, ATP, and oxidative phosphorylation, were the most significant dysregulated genes. In the cerebellum, dysregulated genes were involved in mitochondrial cellular biosynthesis (mitochondrial ribosomes). Although there was little overlap between dysregulated genes between the frontal cortex and cerebellum, machine learning models comprised of this overlap. A further functional network analysis of these genes identified that two downregulated genes, ATP5L and ATP5H, which both encode subunits of ATP synthase (mitochondrial complex V) may play a role in AD. Combined, our results suggest that mitochondrial dysfunction, particularly a deficit in energy homeostasis, may play an important role in AD.
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