ArticleScientific reports2021
Machine learning compensates fold-change method and highlights oxidative phosphorylation in the brain transcriptome of Alzheimer's disease.
Article in Scientific reports, 2021. 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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12 citing papers in PubMed, 25 citations in OpenAlex.
- Exploring Gene Expression Patterns in Alzheimer's Disease Using a Human Microarray Data Meta-Analysis.Biology · 2026Article
- Identifying transcriptomic signatures that mediate the causal effect of genotype on Alzheimer's disease.Frontiers in neuroscience · 2026Article
- Metabolic and Inflammatory Stimuli Impact Vascular Circulation and Cell Proliferation Processes in the Amygdala.Genes, brain, and behavior · 2025Article
- Identification of Key Fatty Acid Metabolism-Related Genes in Alzheimer's Disease.Molecular neurobiology · 2025Article
- Machine learning identified novel players in lipid metabolism, endosomal trafficking, and iron metabolism of the ALS spinal cord.Scientific reports · 2025Article
- Revisiting Fold-Change Calculation: Preference for Median or Geometric Mean over Arithmetic Mean-Based Methods.Biomedicines · 2024Article
- Tracing unknown tumor origins with a biological-pathway-based transformer model.Cell reports methods · 2024Article
- Immune and metabolic challenges induce changes in pain sensation and related pathways in the hypothalamus.Physiological genomics · 2024Article
- A primer on the use of machine learning to distil knowledge from data in biological psychiatry.Molecular psychiatry · 2024Review
- Identification of key lipid metabolism-related genes in Alzheimer's disease.Lipids in health and disease · 2023Article
- Mitochondrial hypermetabolism precedes impaired autophagy and synaptic disorganization in App knock-in Alzheimer mouse models.Molecular psychiatry · 2023Article
- PINNet: a deep neural network with pathway prior knowledge for Alzheimer's disease.Frontiers in aging neuroscience · 2023Article
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4 authors at 2 institutions in 1 country.
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Abstract
Alzheimer's disease (AD) is a neurodegenerative disorder causing 70% of dementia cases. However, the mechanism of disease development is still elusive. Despite the availability of a wide range of biological data, a comprehensive understanding of AD's mechanism from machine learning (ML) is so far unrealized, majorly due to the lack of needed data density. To harness the AD mechanism's knowledge from the expression profiles of postmortem prefrontal cortex samples of 310 AD and 157 controls, we used seven predictive operators or combinations of RapidMiner Studio operators to establish predictive models from the input matrix and to assign a weight to each attribute. Besides, conventional fold-change methods were also applied as controls. The identified genes were further submitted to enrichment analysis for KEGG pathways. The average accuracy of ML models ranges from 86.30% to 91.22%. The overlap ratio of the identified genes between ML and conventional methods ranges from 19.7% to 21.3%. ML exclusively identified oxidative phosphorylation genes in the AD pathway. Our results highlighted the deficiency of oxidative phosphorylation in AD and suggest that ML should be considered as complementary to the conventional fold-change methods in transcriptome studies.
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