2013
Adjusting for Background Mutation Frequency Biases Improves the Identification of Cancer Driver Genes
Evans P, Avey S, Kong Y, Krauthammer M. Adjusting for Background Mutation Frequency Biases Improves the Identification of Cancer Driver Genes. IEEE Transactions On NanoBioscience 2013, 12: 150-157. PMID: 23694700, PMCID: PMC3989533, DOI: 10.1109/tnb.2013.2263391.Peer-Reviewed Original ResearchMeSH KeywordsComputational BiologyGene Expression ProfilingGenes, NeoplasmHumansLoss of HeterozygosityMelanomaModels, GeneticMutationMutation RateTumor Cells, CulturedUltraviolet RaysConceptsMore non-synonymous mutationsMutation frequencyTumor sequencing projectsGene-specific mannerCancer driver genesNon-synonymous mutationsSynonymous mutation ratioMutation biasSequencing projectsBackground mutation frequencyGene expressionDriver genesGenesTumor developmentMutation burdenMutation ratioHigher non-synonymous mutation burdenMutationsMutation countsExpressionBackground frequencyFrequency biasesIdentification
2005
MicroRNA: Biological and Computational Perspective
Kong Y, Han J. MicroRNA: Biological and Computational Perspective. Genomics Proteomics & Bioinformatics 2005, 3: 62-72. PMID: 16393143, PMCID: PMC5172550, DOI: 10.1016/s1672-0229(05)03011-1.Peer-Reviewed Original ResearchMeSH KeywordsAnimalsComputational BiologyGene Expression ProfilingGenomeHumansMicroRNAsNucleic Acid ConformationTranscription, GeneticConceptsAbundant gene familyNon-coding RNAsDiscovery of miRNAMulticellular speciesGene familyMiRNA researchGene expressionBiological processesRegulatory functionsMiRNAComputational approachMicroarray applicationsComputational methodsMiRNAsDiscoveryMicroRNAsRNANucleotidesSpeciesPlantsIndispensable toolExpressionFamily