ArticleFrontiers in genetics2019
Detecting Diagnostic Biomarkers of Alzheimer's Disease by Integrating Gene Expression Data in Six Brain Regions.
Article in Frontiers in genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Integrative Transcriptomic, Network, and Machine Learning Analyses Identify Genistein and Resveratrol-Associated Therapeutic Targets in Alzheimer's Disease.Molecular neurobiology · 2026Article
- LASSO-HHO two-stage hybrid gene selection framework for accurate Alzheimer's disease diagnosis.Scientific reports · 2026Article
- Small data, big challenges: Machine- and deep-learning strategies for data-limited drug discovery.Advanced drug delivery reviews · 2026Review
- Diagnosis of Alzheimer's disease with high accuracy via Petri net modeling of signaling pathways.Scientific reports · 2026Article
- A radiomics model predicts progression from mild cognitive impairment to alzheimer's disease using structural MRI.Scientific reports · 2025Article
- Bind: large-scale biological interaction network discovery through knowledge graph-driven machine learning.Journal of translational medicine · 2025Article
- Deep Learning-Based Prediction of Alzheimer's Disease Using Microarray Gene Expression Data.Biomedicines · 2023Article
- A New Approach for Multimodal Usage of Gene Expression and Its Image Representation for the Detection of Alzheimer's Disease.Biomolecules · 2023Article
- Identifying Network Biomarkers for Alzheimer's Disease Using Single-Cell RNA Sequencing Data.Advances in experimental medicine and biology · 2023Article
- Deep belief network-based approach for detecting Alzheimer's disease using the multi-omics data.Computational and structural biotechnology journal · 2023Article
- Ribosomal proteins are blood biomarkers and associated with CD4+ T cell activation in Alzheimer's disease: a study based on machine learning strategies and scRNA-Seq data validation.American journal of translational research · 2023Article
- A glance through the effects of CD4Computational and structural biotechnology journal · 2023Review
- Prediction of Alzheimer's Disease by a Novel Image-Based Representation of Gene Expression.Genes · 2022Article
- Down-Regulation of Insulin Like Growth Factor 1 Involved in Alzheimer's Disease via MAPK, Ras, and FoxO Signaling Pathways.Oxidative medicine and cellular longevity · 2022Article
- Hyper-graph based sparse canonical correlation analysis for the diagnosis of Alzheimer's disease from multi-dimensional genomic data.Methods (San Diego, Calif.) · 2021Article
- Improving the Classification of Alzheimer's Disease Using Hybrid Gene Selection Pipeline and Deep Learning.Frontiers in genetics · 2021Article
- The Application of Artificial Intelligence in the Genetic Study of Alzheimer's Disease.Aging and disease · 2020Review
- Identification of Dysregulated Genes for Late-Onset Alzheimer's Disease Using Gene Expression Data in Brain.Journal of Alzheimer's disease & Parkinsonism · 2020Article
- Transcriptional Networks of Microglia in Alzheimer's Disease and Insights into Pathogenesis.Genes · 2019Review
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2 authors.
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Abstract
Alzheimer's disease (AD) is a neurodegenerative and progressive disease, which often causes irreversible damages to the cerebrum. The pathogenesis of AD is far from being fully understood, while there are some popular hypotheses. So far, the diagnosis of AD relies only on clinical screening in the form of imaging techniques or cerebrospinal fluid analysis, which may lead to inaccurate evaluation and then cause the delay of suitable treatments. While molecular biomarkers provide promising alternatives of establishing correct relationships between genotypes and phenotypes of clinical symptoms. In this paper, we propose a machine-learning-based method of identifying potential diagnostic biomarkers of AD based on gene coexpression network by integrating gene expression profiles in six brain regions. After building an integrated gene coexpression network of multiple brain regions, we decompose the differential network into some subnetwork modules. The module candidates from these coexpressed gene communities are then identified by screening their discriminative powers in control from disease samples. The potential biomarkers are then validated by multiple cross-validations and functional enrichment analyses. If the biomarkers successfully pass clinical significance tests, they can be used as a reference for clinical diagnosis after wet-experimental validations.
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