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1. Analysis of missing data with random forests


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2. Variable selection with Random Forests for missing data


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3. Random Forest variable importance with missing data


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4. Expression Profiling of Stem Cell-Related Genes in Neoadjuvant-Treated Gastric Cancer: A NOTCH2, GSK3B and β-catenin Gene Signature Predicts Survival

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3438181
http://www.ncbi.nlm.nih.gov/pubmed/22970250  
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5. Noninvasive continuous versus intermittent arterial pressure monitoring: evaluation of the vascular unloading technique (CNAP device) in the emergency department

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3909911
http://www.ncbi.nlm.nih.gov/pubmed/24472659  
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6. Intra- and interobserver variability of language mapping by navigated transcranial magnetic brain stimulation

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC
http://www.ncbi.nlm.nih.gov/pubmed/24304865  
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7. Noninvasive continuous versus intermittent arterial pressure monitoring: evaluation of the vascular unloading technique (CNAP device) in the emergency department

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8. Intra- and interobserver variability of language mapping by navigated transcranial magnetic brain stimulation

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9. Inter- and intraobserver variability in motor mapping of the hotspot for the abductor policis brevis muscle

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10. Estimation of a Predictor’s Importance by Random Forests When There Is Missing Data: RISK Prediction in Liver Surgery using Laboratory Data


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