2013
Bayesian Mixture Models for Assessment of Gene Differential Behaviour and Prediction of pCR through the Integration of Copy Number and Gene Expression Data
Trentini F, Ji Y, Iwamoto T, Qi Y, Pusztai L, Müller P. Bayesian Mixture Models for Assessment of Gene Differential Behaviour and Prediction of pCR through the Integration of Copy Number and Gene Expression Data. PLOS ONE 2013, 8: e68071. PMID: 23874497, PMCID: PMC3709899, DOI: 10.1371/journal.pone.0068071.Peer-Reviewed Original Research
2012
A network-based, integrative study to identify core biological pathways that drive breast cancer clinical subtypes
Dutta B, Pusztai L, Qi Y, André F, Lazar V, Bianchini G, Ueno N, Agarwal R, Wang B, Shiang CY, Hortobagyi GN, Mills GB, Symmans WF, Balázsi G. A network-based, integrative study to identify core biological pathways that drive breast cancer clinical subtypes. British Journal Of Cancer 2012, 106: 1107-1116. PMID: 22343619, PMCID: PMC3304402, DOI: 10.1038/bjc.2011.584.Peer-Reviewed Original ResearchMeSH KeywordsBreast NeoplasmsCell Line, TumorComputer SimulationDNA Copy Number VariationsEpithelial-Mesenchymal TransitionFemaleGene ExpressionGene Expression ProfilingGene Expression Regulation, NeoplasticGene Knockdown TechniquesGene Regulatory NetworksGenes, NeoplasmHumansModels, BiologicalProtein Interaction MapsReceptor, ErbB-2Receptors, EstrogenReceptors, ProgesteroneRNA InterferenceConceptsGenome-scale dataCore biological pathwaysTriple receptor-negative breast cancerProtein-protein interactionsCell line data setsGene knockdown experimentsGene copy number dataCopy number dataCopy number variation dataNumber variation dataMember genesGene networksTranscriptional disturbancesKnockdown experimentsBiological discoveryGene expressionFunctional specificityBiological pathwaysDifferential expressionIntegrative studyFunctional relevanceVariation dataLine data setsCell linesGenes