An efficient Monte Carlo approach to assessing statistical significance in genomic studies.
Lin
D Y
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Department of Biostatistics, University of North Carolina, McGavran-Greenberg Hall, CB #7420, Chapel Hill, NC 27599-7420, USA. lin@bios.unc.edu
D000465:Algorithms; D014408:Biomarkers, Tumor / Q000378:metabolism; D002874:Chromosome Mapping / Q000379:methods*; D003627:Data Interpretation, Statistical; D020869:Gene Expression Profiling / Q000379:methods*; D023281:Genomics / Q000379:methods; D006801:Humans; D008175:Lung Neoplasms / Q000175:diagnosis / Q000235:genetics / Q000378:metabolism; D008957:Models, Genetic; D015233:Models, Statistical; D009010:Monte Carlo Method; D009363:Neoplasm Proteins / Q000235:genetics / Q000378:metabolism; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D017422:Sequence Analysis, DNA / Q000379:methods*
D014408:Biomarkers, Tumor; D009363:Neoplasm Proteins