ArticleGenetic epidemiology2008
Application of machine learning algorithms to predict coronary artery calcification with a sibship-based design.
Article in Genetic epidemiology, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
What it found
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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.
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.
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Who cites it
37 citing papers in PubMed, 71 citations in OpenAlex.
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- GPR35 protects against reperfusion injury in ischemic stroke by binding with the CR2 domain of Raf1.Journal of neuroinflammation · 2026Article
- Review
- Processed silkworm powder (Hongjam) ameliorates metabolic dysfunction-associated steatotic liver disease via GPR35/PKA and SIRT1/AMPK pathways.Frontiers in nutrition · 2025Article
- Using machine learning-based algorithms to construct cardiovascular risk prediction models for Taiwanese adults based on traditional and novel risk factors.BMC medical informatics and decision making · 2024Article
- GPR35 and mediators from platelets and mast cells in neutrophil migration and inflammation.Immunological reviews · 2023Review
- Recent advances in GPR35 pharmacology; 5-HIAA serotonin metabolite becomes a ligand.Archives of pharmacal research · 2023Review
- G protein-coupled receptor 35 attenuates nonalcoholic steatohepatitis by reprogramming cholesterol homeostasis in hepatocytes.Acta pharmaceutica Sinica. B · 2023Article
- Blood Pressure Prediction Using Ensemble Rules during Isometric Sustained Weight Test.Journal of cardiovascular development and disease · 2022Article
- Machine Learning Approach for Cardiovascular Risk and Coronary Artery Calcification Score.BioMed research international · 2022Article
- RuleFit-Based Nomogram Using Inflammatory Indicators for Predicting Survival in Nasopharyngeal Carcinoma, a Bi-Center Study.Journal of inflammation research · 2022Article
- Novel Role of GPR35 (G-Protein-Coupled Receptor 35) in the Regulation of Endothelial Cell Function and Blood Pressure.Hypertension (Dallas, Tex. : 1979) · 2021Article
- Disruption of GPR35 Signaling in Bone Marrow-Derived Cells Does Not Influence Vascular Inflammation and Atherosclerosis in Hyperlipidemic Mice.Metabolites · 2021Article
- Reviewing the relationship between machines and radiology: the application of artificial intelligence.Acta radiologica open · 2021Review
- GPR35 in Intestinal Diseases: From Risk Gene to Function.Frontiers in immunology · 2021Review
- Therapeutic Opportunities and Challenges in Targeting the Orphan G Protein-Coupled Receptor GPR35.ACS pharmacology & translational science · 2020Review
- Inhibition of GPR35 Preserves Mitochondrial Function After Myocardial Infarction by Targeting Calpain 1/2.Journal of cardiovascular pharmacology · 2020Article
- Estimating the heritability of psychological measures in the Human Connectome Project dataset.PloS one · 2020Article
- Disruption of GPR35 Exacerbates Dextran Sulfate Sodium-Induced Colitis in Mice.Digestive diseases and sciences · 2018Article
- The Orphan Receptor GPR35 Contributes to Angiotensin II-Induced Hypertension and Cardiac Dysfunction in Mice.American journal of hypertension · 2018Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
Abstract
As part of the Genetic Epidemiology Network of Arteriopathy study, hypertensive non-Hispanic White sibships were screened using 471 single nucleotide polymorphisms (SNPs) to identify genes influencing coronary artery calcification (CAC) measured by computed tomography. Individuals with detectable CAC and CAC quantity > or =70th age- and sex-specific percentile were classified as having a high CAC burden and compared to individuals with CAC quantity <70th percentile. Two sibs from each sibship were randomly chosen and divided into two data sets, each with 360 unrelated individuals. Within each data set, we applied two machine learning algorithms, Random Forests and RuleFit, to identify the best predictors of having high CAC burden among 17 risk factors and 471 SNPs. Using five-fold cross-validation, both methods had approximately 70% sensitivity and approximately 60% specificity. Prediction accuracies were significantly different from random predictions (P-value<0.001) based on 1,000 permutation tests. Predictability of using 287 tagSNPs was as good as using all 471 SNPs. For Random Forests, among the top 50 predictors, the same eight tagSNPs and 15 risk factors were found in both data sets while eight tagSNPs and 12 risk factors were found in both data sets for RuleFit. Replicable effects of two tagSNPs (in genes GPR35 and NOS3) and 12 risk factors (age, body mass index, sex, serum glucose, high-density lipoprotein cholesterol, systolic blood pressure, cholesterol, homocysteine, triglycerides, fibrinogen, Lp(a) and low-density lipoprotein particle size) were identified by both methods. This study illustrates how machine learning methods can be used in sibships to identify important, replicable predictors of subclinical coronary atherosclerosis.
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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.