ArticleBMC genetics2010
Identification of type 2 diabetes-associated combination of SNPs using support vector machine.
Article in BMC genetics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
35 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Machine learning and artificial intelligence in type 2 diabetes prediction: a comprehensive 33-year bibliometric and literature analysis.Frontiers in digital health · 2025Pooled it
- Association between -308G/A TNFA Polymorphism and Susceptibility to Type 2 Diabetes Mellitus: A Systematic Review.Journal of diabetes research · 2016Pooled it
- Advances in Artificial Intelligence (AI) Models and Generative Algorithms Represent a New Paradigm for Genomics Research.International journal of molecular sciences · 2025Review
- The classification method of donkey breeds based on SNPs data and machine learning.Frontiers in genetics · 2025Article
- Machine Learning-Based Identification of Mating Type and Metalaxyl Response inMicroorganisms · 2024Article
- Prenatal benzene exposure in mice alters offspring hypothalamic development predisposing to metabolic disease in later life.Chemosphere · 2023Article
- Article
- Eye-color and Type-2 diabetes phenotype prediction from genotype data using deep learning methods.BMC bioinformatics · 2021Article
- Intermuscular Fat Content in Young Chinese Men With Newly Diagnosed Type 2 Diabetes: Based on MR mDIXON-Quant Quantitative Technique.Frontiers in endocrinology · 2021Article
- Prediction of Smoking Behavior From Single Nucleotide Polymorphisms With Machine Learning Approaches.Frontiers in psychiatry · 2020Article
- GWAS for Meat and Carcass Traits Using Imputed Sequence Level Genotypes in Pooled F2-Designs in Pigs.G3 (Bethesda, Md.) · 2019Article
- Cross-population analysis for functional characterization of type II diabetes variants.BMC bioinformatics · 2019Article
- A single nucleotide polymorphism panel for individual identification and ancestry assignment in Caucasians and four East and Southeast Asian populations using a machine learning classifier.Forensic science, medicine, and pathology · 2019Article
- Predictive modeling of schizophrenia from genomic data: Comparison of polygenic risk score with kernel support vector machines approach.American journal of medical genetics. Part B, Neuropsychiatric genetics : the official publication of the International Society of Psychiatric Genetics · 2019Article
- Pan-Genomic and Polymorphic Driven Prediction of Antibiotic Resistance inFrontiers in microbiology · 2019Article
- Pipeline design to identify key features and classify the chemotherapy response on lung cancer patients using large-scale genetic data.BMC systems biology · 2018Article
- KNN-MDR: a learning approach for improving interactions mapping performances in genome wide association studies.BMC bioinformatics · 2017Article
- Statistical and Computational Methods for Genetic Diseases: An Overview.Computational and mathematical methods in medicine · 2015Review
- Genome-wide association studies identified novel loci for non-high-density lipoprotein cholesterol and its postprandial lipemic response.Human genetics · 2014Article
- Identifying disease associated genes by network propagation.BMC systems biology · 2014Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundType 2 diabetes mellitus (T2D), a metabolic disorder characterized by insulin resistance and relative insulin deficiency, is a complex disease of major public health importance. Its incidence is rapidly increasing in the developed countries. Complex diseases are caused by interactions between multiple genes and environmental factors. Most association studies aim to identify individual susceptibility single markers using a simple disease model. Recent studies are trying to estimate the effects of multiple genes and multi-locus in genome-wide association. However, estimating the effects of association is very difficult. We aim to assess the rules for classifying diseased and normal subjects by evaluating potential gene-gene interactions in the same or distinct biological pathways.
resultsWe analyzed the importance of gene-gene interactions in T2D susceptibility by investigating 408 single nucleotide polymorphisms (SNPs) in 87 genes involved in major T2D-related pathways in 462 T2D patients and 456 healthy controls from the Korean cohort studies. We evaluated the support vector machine (SVM) method to differentiate between cases and controls using SNP information in a 10-fold cross-validation test. We achieved a 65.3% prediction rate with a combination of 14 SNPs in 12 genes by using the radial basis function (RBF)-kernel SVM. Similarly, we investigated subpopulation data sets of men and women and identified different SNP combinations with the prediction rates of 70.9% and 70.6%, respectively. As the high-throughput technology for genome-wide SNPs improves, it is likely that a much higher prediction rate with biologically more interesting combination of SNPs can be acquired by using this method.
conclusionsSupport Vector Machine based feature selection method in this research found novel association between combinations of SNPs and T2D in a Korean population.
Indexed as
Identifiers
What OpenQuestion holds
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.