An efficient Monte Carlo approach to assessing statistical significance in genomic studies.
Lin, D Y
An efficient Monte Carlo approach to assessing statistical significance in genomic studies. [electronic resource] - Bioinformatics (Oxford, England) Mar 2005 - 781-7 p. digital
Publication Type: Comparative Study; Evaluation Study; Journal Article; Research Support, U.S. Gov't, P.H.S.
1367-4803
10.1093/bioinformatics/bti053 doi
Algorithms
Biomarkers, Tumor--metabolism
Chromosome Mapping--methods
Data Interpretation, Statistical
Gene Expression Profiling--methods
Genomics--methods
Humans
Lung Neoplasms--diagnosis
Models, Genetic
Models, Statistical
Monte Carlo Method
Neoplasm Proteins--genetics
Oligonucleotide Array Sequence Analysis--methods
Reproducibility of Results
Sensitivity and Specificity
Sequence Analysis, DNA--methods
An efficient Monte Carlo approach to assessing statistical significance in genomic studies. [electronic resource] - Bioinformatics (Oxford, England) Mar 2005 - 781-7 p. digital
Publication Type: Comparative Study; Evaluation Study; Journal Article; Research Support, U.S. Gov't, P.H.S.
1367-4803
10.1093/bioinformatics/bti053 doi
Algorithms
Biomarkers, Tumor--metabolism
Chromosome Mapping--methods
Data Interpretation, Statistical
Gene Expression Profiling--methods
Genomics--methods
Humans
Lung Neoplasms--diagnosis
Models, Genetic
Models, Statistical
Monte Carlo Method
Neoplasm Proteins--genetics
Oligonucleotide Array Sequence Analysis--methods
Reproducibility of Results
Sensitivity and Specificity
Sequence Analysis, DNA--methods