ArticleAdvances in bioinformatics2015
Discovering Alzheimer Genetic Biomarkers Using Bayesian Networks.
Article in Advances in bioinformatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Methods for Prioritizing Causal Genes in Molecular Studies of Human Disease: The State of the Art.Genetic epidemiology · 2026Review
- Exploring coral reef communities in Puerto Rico using Bayesian networks.Ecological informatics · 2024Article
- Deep Learning-Based Prediction of Alzheimer's Disease Using Microarray Gene Expression Data.Biomedicines · 2023Article
- Review
- Synthetic data generation with probabilistic Bayesian Networks.Mathematical biosciences and engineering : MBE · 2021Article
- Genetic variations analysis for complex brain disease diagnosis using machine learning techniques: opportunities and hurdles.PeerJ. Computer science · 2021Article
- Machine learning for the life-time risk prediction of Alzheimer's disease: a systematic review.Brain communications · 2021Review
- Epi-GTBN: an approach of epistasis mining based on genetic Tabu algorithm and Bayesian network.BMC bioinformatics · 2019Article
- The Role of Genetic Factors in Characterizing Extra-Intestinal Manifestations in Crohn's Disease Patients: Are Bayesian Machine Learning Methods Improving Outcome Predictions?Journal of clinical medicine · 2019Article
- Predictive models for pressure ulcers from intensive care unit electronic health records using Bayesian networks.BMC medical informatics and decision making · 2017Article
- Recent publications from the Alzheimer's Disease Neuroimaging Initiative: Reviewing progress toward improved AD clinical trials.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2017Review
- New Algorithm and Software (BNOmics) for Inferring and Visualizing Bayesian Networks from Heterogeneous Big Biological and Genetic Data.Journal of computational biology : a journal of computational molecular cell biology · 2017Article
- Verification of Three-Phase Dependency Analysis Bayesian Network Learning Method for Maize Carotenoid Gene Mining.BioMed research international · 2017Article
- Geographical distribution of complement receptor type 1 variants and their associated disease risk.PloS one · 2017Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Single nucleotide polymorphisms (SNPs) contribute most of the genetic variation to the human genome. SNPs associate with many complex and common diseases like Alzheimer's disease (AD). Discovering SNP biomarkers at different loci can improve early diagnosis and treatment of these diseases. Bayesian network provides a comprehensible and modular framework for representing interactions between genes or single SNPs. Here, different Bayesian network structure learning algorithms have been applied in whole genome sequencing (WGS) data for detecting the causal AD SNPs and gene-SNP interactions. We focused on polymorphisms in the top ten genes associated with AD and identified by genome-wide association (GWA) studies. New SNP biomarkers were observed to be significantly associated with Alzheimer's disease. These SNPs are rs7530069, rs113464261, rs114506298, rs73504429, rs7929589, rs76306710, and rs668134. The obtained results demonstrated the effectiveness of using BN for identifying AD causal SNPs with acceptable accuracy. The results guarantee that the SNP set detected by Markov blanket based methods has a strong association with AD disease and achieves better performance than both naïve Bayes and tree augmented naïve Bayes. Minimal augmented Markov blanket reaches accuracy of 66.13% and sensitivity of 88.87% versus 61.58% and 59.43% in naïve Bayes, respectively.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.