ArticleBMC neurology2018
Revealing Alzheimer's disease genes spectrum in the whole-genome by machine learning.
Article in BMC neurology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
17 citing papers in PubMed.
- Machine Learning-Based Alzheimer's Disease Stage Diagnosis Utilizing Blood Gene Expression and Clinical Data: A Comparative Investigation.Diagnostics (Basel, Switzerland) · 2025Article
- AlzGenPred - CatBoost-based gene classifier for predicting Alzheimer's disease using high-throughput sequencing data.Scientific reports · 2024Article
- Etiology of Late-Onset Alzheimer's Disease, Biomarker Efficacy, and the Role of Machine Learning in Stage Diagnosis.Diagnostics (Basel, Switzerland) · 2024Review
- LIMO-GCN: a linear model-integrated graph convolutional network for predicting Alzheimer disease genes.Briefings in bioinformatics · 2024Article
- G-Protein Signaling in Alzheimer's Disease: Spatial Expression Validation of Semi-supervised Deep Learning-Based Computational Framework.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2024Article
- Identifying Effective Feature Selection Methods for Alzheimer's Disease Biomarker Gene Detection Using Machine Learning.Diagnostics (Basel, Switzerland) · 2023Article
- The Construction of a Multidomain Risk Model of Alzheimer's Disease and Related Dementias.Journal of Alzheimer's disease : JAD · 2023Article
- Machine learning prediction and tau-based screening identifies potential Alzheimer's disease genes relevant to immunity.Communications biology · 2022Article
- TissueNexus: a database of human tissue functional gene networks built with a large compendium of curated RNA-seq data.Nucleic acids research · 2022Article
- 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
- Applications of machine learning to diagnosis and treatment of neurodegenerative diseases.Nature reviews. Neurology · 2020Review
- A multiomics approach to heterogeneity in Alzheimer's disease: focused review and roadmap.Brain : a journal of neurology · 2020Review
- Data science in neurodegenerative disease: its capabilities, limitations, and perspectives.Current opinion in neurology · 2020Review
- Exploring Alzheimer's Disease Molecular Variability via Calculation of Personalized Transcriptional Signatures.Biomolecules · 2020Article
- Identification of Dysregulated Genes for Late-Onset Alzheimer's Disease Using Gene Expression Data in Brain.Journal of Alzheimer's disease & Parkinsonism · 2020Article
- Tackling gaps in developing life-changing treatments for dementia.Alzheimer's & dementia (New York, N. Y.) · 2019Review
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
backgroundAlzheimer's disease (AD) is an important, progressive neurodegenerative disease, with a complex genetic architecture. A key goal of biomedical research is to seek out disease risk genes, and to elucidate the function of these risk genes in the development of disease. For this purpose, expanding the AD-associated gene set is necessary. In past research, the prediction methods for AD related genes has been limited in their exploration of the target genome regions. We here present a genome-wide method for AD candidate genes predictions.
methodsWe present a machine learning approach (SVM), based upon integrating gene expression data with human brain-specific gene network data, to discover the full spectrum of AD genes across the whole genome.
resultsWe classified AD candidate genes with an accuracy and the area under the receiver operating characteristic (ROC) curve of 84.56% and 94%. Our approach provides a supplement for the spectrum of AD-associated genes extracted from more than 20,000 genes in a genome wide scale.
conclusionsIn this study, we have elucidated the whole-genome spectrum of AD, using a machine learning approach. Through this method, we expect for the candidate gene catalogue to provide a more comprehensive annotation of AD for researchers.
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