ArticleHuman heredity2011
Power of data mining methods to detect genetic associations and interactions.
Article in Human heredity, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 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
14 citing papers in PubMed, 25 citations in OpenAlex.
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- Genomics models in radiotherapy: From mechanistic to machine learning.Medical physics · 2020Review
- Identification of epistasis loci underlying rice flowering time by controlling population stratification and polygenic effect.DNA research : an international journal for rapid publication of reports on genes and genomes · 2019Article
- Investigating Gene-Gene and Gene-Environment Interactions in the Association Between Overnutrition and Obesity-Related Phenotypes.Frontiers in genetics · 2019Article
- Machine Learning and Radiogenomics: Lessons Learned and Future Directions.Frontiers in oncology · 2018Review
- Exploiting Linkage Disequilibrium for Ultrahigh-Dimensional Genome-Wide Data with an Integrated Statistical Approach.Genetics · 2016Article
- Prediction of remission in obsessive compulsive disorder using a novel machine learning strategy.International journal of methods in psychiatric research · 2015Article
- Evaluating methods for modeling epistasis networks with application to head and neck cancer.Cancer informatics · 2015Article
- Detecting epistasis in human complex traits.Nature reviews. Genetics · 2014Review
- The association of alcohol intake with γ-glutamyl transferase (GGT) levels: evidence for correlated genetic effects.Drug and alcohol dependence · 2014Article
- Genetic interaction of GSH metabolic pathway genes in cystic fibrosis.BMC medical genetics · 2013Article
- Finding type 2 diabetes causal single nucleotide polymorphism combinations and functional modules from genome-wide association data.BMC medical informatics and decision making · 2013Article
- Random forests on Hadoop for genome-wide association studies of multivariate neuroimaging phenotypes.BMC bioinformatics · 2013Article
- Random forests for genomic data analysis.Genomics · 2012Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundGenetic association studies, thus far, have focused on the analysis of individual main effects of SNP markers. Nonetheless, there is a clear need for modeling epistasis or gene-gene interactions to better understand the biologic basis of existing associations. Tree-based methods have been widely studied as tools for building prediction models based on complex variable interactions. An understanding of the power of such methods for the discovery of genetic associations in the presence of complex interactions is of great importance. Here, we systematically evaluate the power of three leading algorithms: random forests (RF), Monte Carlo logic regression (MCLR), and multifactor dimensionality reduction (MDR).
methodsWe use the algorithm-specific variable importance measures (VIMs) as statistics and employ permutation-based resampling to generate the null distribution and associated p values. The power of the three is assessed via simulation studies. Additionally, in a data analysis, we evaluate the associations between individual SNPs in pro-inflammatory and immunoregulatory genes and the risk of non-Hodgkin lymphoma.
resultsThe power of RF is highest in all simulation models, that of MCLR is similar to RF in half, and that of MDR is consistently the lowest.
conclusionsOur study indicates that the power of RF VIMs is most reliable. However, in addition to tuning parameters, the power of RF is notably influenced by the type of variable (continuous vs. categorical) and the chosen VIM.
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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.