ArticlePloS one2015
Networks in Coronary Heart Disease Genetics As a Step towards Systems Epidemiology.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed.
- Machine Learning Algorithm Predicts Cardiac Resynchronization Therapy Outcomes: Lessons From the COMPANION Trial.Circulation. Arrhythmia and electrophysiology · 2018Trial
- CYP4V2 rs56413992 C > T was associated with the risk of coronary heart disease in the Chinese Han population: a case-control study.BMC medical genomics · 2023Article
- Artificial intelligence predicts clinically relevant atrial high-rate episodes in patients with cardiac implantable electronic devices.Scientific reports · 2022Observational
- The role of exogenous Fibrinogen in cardiac surgery: stop bleeding or induce cardiovascular disease.Molecular biology reports · 2020Review
- Prediction of Resting Energy Expenditure in Children: May Artificial Neural Networks Improve Our Accuracy?Journal of clinical medicine · 2020Article
- The effect of neighborhood social environment on prostate cancer development in black and white men at high risk for prostate cancer.PloS one · 2020Article
- Magic Moments: Determinants of Stress Relief and Subjective Wellbeing from Visiting a Cultural Heritage Site.Culture, medicine and psychiatry · 2019Observational
- Article
- A call for biological data mining approaches in epidemiology.BioData mining · 2016Article
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
Abstract
We present the use of innovative machine learning techniques in the understanding of Coronary Heart Disease (CHD) through intermediate traits, as an example of the use of this class of methods as a first step towards a systems epidemiology approach of complex diseases genetics. Using a sample of 252 middle-aged men, of which 102 had a CHD event in 10 years follow-up, we applied machine learning algorithms for the selection of CHD intermediate phenotypes, established markers, risk factors, and their previously associated genetic polymorphisms, and constructed a map of relationships between the selected variables. Of the 52 variables considered, 42 were retained after selection of the most informative variables for CHD. The constructed map suggests that most selected variables were related to CHD in a context dependent manner while only a small number of variables were related to a specific outcome. We also observed that loss of complexity in the network was linked to a future CHD event. We propose that novel, non-linear, and integrative epidemiological approaches are required to combine all available information, in order to truly translate the new advances in medical sciences to gains in preventive measures and patients care.
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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.