ArticleMolecular genetics and genomics : MGG2021
Classifying single nucleotide polymorphisms in humans.
Article in Molecular genetics and genomics : MGG, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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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.
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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
8 citing papers in PubMed, 1 synthesis or guideline pooled it, 37 citations in OpenAlex.
- The pertinence of gastric cancer and interleukin 10-819 single nucleotide polymorphisms: a meta-analysis and systematic review.BMC gastroenterology · 2024Pooled it
- Genetic Polymorphisms as Modifiers of Health Risks from Exposure to Toxic Elements: A Traditional Literature Review.Toxics · 2026Review
- Genetic association of miR-146a, miR-196a2, and miR-499 polymorphisms with hepatocellular carcinoma risk in an Eastern Chinese population.Frontiers in oncology · 2026Article
- Genomic characterization of Salmonella enterica isolates causing typhoid among Ghanaian patients.PloS one · 2026Article
- Molecular markers associated with elevated colorectal cancer risk: a mini review.Hereditary cancer in clinical practice · 2025Review
- GC vitamin D-binding protein gene functional genetic variants and gestational diabetes mellitus risk and prediction.Scientific reports · 2025Article
- Interplay between BMI, neutrophil, triglyceride and uric acid: a case-control study and bidirectional multivariate mendelian randomization analysis.Nutrition & metabolism · 2025Article
- CSPG4P12 polymorphism served as a susceptibility marker for esophageal cancer in Chinese population.BMC cancer · 2024Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Single nucleotide polymorphisms (SNPs) are the most common form of genetic variation amongst the human population and are key to personalized medicine. New tests are presented to distinguish pathogenic/malign (i.e., likely to contribute to or cause a disease) from nonpathogenic/benign SNPs, regardless of whether they occur in coding (exon) or noncoding (intron) regions in the human genome. The tests are based on the nearest neighbor (NN) model of Gibbs free energy landscapes of DNA hybridization and on deep structural properties of DNA revealed by an approximating metric (the h-distance) in DNA spaces of oligonucleotides of a common size. The quality assessments show that the newly defined PNPG test can classify a SNP with an accuracy about 73% for the required parameters. The best performance among machine learning models is a feed-forward neural network with fivefold cross-validation accuracy of at least 73%. These results may provide valuable tools to solve the SNP classification problem, where tools are lacking, to assess the likelihood of disease causing in unclassified SNPs. These tests highlight the significance of hybridization chemistry in SNPs. They can be applied to further the effectiveness of research in the areas of genomics and metabolomics.
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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.