ArticleBioinformatics (Oxford, England)2008
EM-random forest and new measures of variable importance for multi-locus quantitative trait linkage analysis.
Article in Bioinformatics (Oxford, England), 2008. 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.
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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
9 citing papers in PubMed.
- Application of Artificial Intelligence in Screening for Adverse Perinatal Outcomes-A Systematic Review.Healthcare (Basel, Switzerland) · 2022Review
- An enhanced machine learning tool for cis-eQTL mapping with regularization and confounder adjustments.Genetic epidemiology · 2020Article
- Application of data mining for predicting hemodynamics instability during pheochromocytoma surgery.BMC medical informatics and decision making · 2020Article
- An experimental study of the intrinsic stability of random forest variable importance measures.BMC bioinformatics · 2016Article
- Article
- Impact of natural genetic variation on gene expression dynamics.PLoS genetics · 2013Article
- Random forests for genetic association studies.Statistical applications in genetics and molecular biology · 2011Review
- Data-driven assessment of eQTL mapping methods.BMC genomics · 2010Article
- Genome-wide strategies for discovering genetic influences on cognition and cognitive disorders: methodological considerations.Cognitive neuropsychiatry · 2009Article
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
motivationWe developed an EM-random forest (EMRF) for Haseman-Elston quantitative trait linkage analysis that accounts for marker ambiguity and weighs each sib-pair according to the posterior identical by descent (IBD) distribution. The usual random forest (RF) variable importance (VI) index used to rank markers for variable selection is not optimal when applied to linkage data because of correlation between markers. We define new VI indices that borrow information from linked markers using the correlation structure inherent in IBD linkage data.
resultsUsing simulations, we find that the new VI indices in EMRF performed better than the original RF VI index and performed similarly or better than EM-Haseman-Elston regression LOD score for various genetic models. Moreover, tree size and markers subset size evaluated at each node are important considerations in RFs. AVAILABILITY: The source code for EMRF written in C is available at www.infornomics.utoronto.ca/downloads/EMRF.
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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 Socratic graph.