Power of Data Mining Methods to Detect Genetic Associations and Interactions
Molinaro AM, Carriero N, Bjornson R, Hartge P, Rothman N, Chatterjee N. Power of Data Mining Methods to Detect Genetic Associations and Interactions. Human Heredity 2011, 72: 85-97. PMID: 21934324, PMCID: PMC3222116, DOI: 10.1159/000330579.Peer-Reviewed Original ResearchConceptsMonte Carlo logic regressionRandom forestVariable importance measuresRF variable importance measuresData mining methodsComplex variable interactionsMining methodsTree-based methodsDimensionality reductionPrediction modelSuch methodsImportance measuresLogic regressionSimulation modelMultifactor dimensionality reductionData analysisVariable interactionsAlgorithmSimulation studyAlleleSeq: analysis of allele‐specific expression and binding in a network framework
Rozowsky J, Abyzov A, Wang J, Alves P, Raha D, Harmanci A, Leng J, Bjornson R, Kong Y, Kitabayashi N, Bhardwaj N, Rubin M, Snyder M, Gerstein M. AlleleSeq: analysis of allele‐specific expression and binding in a network framework. Molecular Systems Biology 2011, 7: msb201154. PMID: 21811232, PMCID: PMC3208341, DOI: 10.1038/msb.2011.54.Peer-Reviewed Original ResearchMeSH KeywordsAllelesCell LineChromosome MappingChromosomes, Human, XChromosomes, Human, YDatabases, GeneticDNA-Binding ProteinsGene Expression RegulationGene Regulatory NetworksGenome, HumanHumansMolecular Sequence AnnotationOligonucleotide Array Sequence AnalysisPolymorphism, Single NucleotideSequence Analysis, RNATranscription FactorsConceptsAllele-specific expressionGenome sequenceFunctional genomics data setsAllele-specific behaviorAllele-specific eventsDiploid genome sequenceChIP-seq data setsGenomic data setsGenomic sequence variantsPersonal genome sequencesAlignment of readsRNA-seqGenome ProjectPaternal alleleComputational pipelineReads mappingSequence variantsNetwork motifsVariation dataReference alleleAllelesReadsSequenceExpressionMaternally