2014
Computational Analysis in Cancer Exome Sequencing
Evans P, Kong Y, Krauthammer M. Computational Analysis in Cancer Exome Sequencing. Methods In Molecular Biology 2014, 1176: 219-227. PMID: 25030931, DOI: 10.1007/978-1-4939-0992-6_18.Peer-Reviewed Original ResearchConceptsSomatic single nucleotide variantsMutational eventsSingle nucleotide variantsHuman genesSequencing readsShort insertionsDriver genesNucleotide variantsNumber alterationsExome sequencingGenesCancer samplesComputational analysisMore mutational eventsPowerful toolComputational methodsExomeSequencingDeletionReads
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 ResearchConceptsMore 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
2009
Calculating complexity of large randomized libraries
Kong Y. Calculating complexity of large randomized libraries. Journal Of Theoretical Biology 2009, 259: 641-645. PMID: 19376134, DOI: 10.1016/j.jtbi.2009.04.008.Peer-Reviewed Original Research
2007
Keck Foundation Biotechnology Resource Laboratory, Yale University.
Stone KL, Bjornson RD, Blasko GG, Bruce C, Cofrancesco R, Carriero NJ, Colangelo CM, Crawford JK, Crawford JM, daSilva NC, Deluca JD, Elliott JI, Elliott MM, Flory PJ, Folta-Stogniew EJ, Gulcicek E, Kong Y, Lam TT, Lee JY, Lin A, LoPresti MB, Mane SM, McMurray WJ, Tikhonova IR, Westman S, Williams NA, Wu TL, Hongyu Z, Williams KR. Keck Foundation Biotechnology Resource Laboratory, Yale University. The Yale Journal Of Biology And Medicine 2007, 80: 195-211. PMID: 18449392, PMCID: PMC2347368.Peer-Reviewed Original ResearchGeneralized Correlation Functions and Their Applications in Selection of Optimal Multiple Spaced Seeds for Homology Search
Kong Y. Generalized Correlation Functions and Their Applications in Selection of Optimal Multiple Spaced Seeds for Homology Search. Journal Of Computational Biology 2007, 14: 238-254. PMID: 17456017, DOI: 10.1089/cmb.2006.0008.Peer-Reviewed Original ResearchMeSH KeywordsAnimalsComputational BiologyEscherichia coliGenomeHaemophilus influenzaeHumansMiceModels, GeneticSequence Analysis, DNASequence Homology, Nucleic AcidConceptsGeneralized correlation functionCorrelation functionsHigher order approximationsGoulden–Jackson cluster methodHeuristic search methodsOrder approximationProbability qAverage propertiesSearch methodCluster methodLarge genomic dataProbability of occurrenceTheoretical backgroundMultiple seedsSpaced seedsPowerful methodOptimal seedApproximationEmpirical observationsNumber of wildcardsSet of patternsProbabilityProblemFunctionMatrix
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
2003
A Protein Interaction Map of Drosophila melanogaster
Giot L, Bader JS, Brouwer C, Chaudhuri A, Kuang B, Li Y, Hao YL, Ooi CE, Godwin B, Vitols E, Vijayadamodar G, Pochart P, Machineni H, Welsh M, Kong Y, Zerhusen B, Malcolm R, Varrone Z, Collis A, Minto M, Burgess S, McDaniel L, Stimpson E, Spriggs F, Williams J, Neurath K, Ioime N, Agee M, Voss E, Furtak K, Renzulli R, Aanensen N, Carrolla S, Bickelhaupt E, Lazovatsky Y, DaSilva A, Zhong J, Stanyon CA, Finley RL, White KP, Braverman M, Jarvie T, Gold S, Leach M, Knight J, Shimkets RA, McKenna MP, Chant J, Rothberg JM. A Protein Interaction Map of Drosophila melanogaster. Science 2003, 302: 1727-1736. PMID: 14605208, DOI: 10.1126/science.1090289.Peer-Reviewed Original ResearchMeSH KeywordsAnimalsCalciumCell CycleCell DifferentiationCloning, MolecularComputational BiologyDNA, ComplementaryDrosophila melanogasterDrosophila ProteinsErbB ReceptorsGenes, InsectImmunity, InnateMathematicsModels, StatisticalPhotoreceptor Cells, InvertebrateProtein BindingProtein Interaction MappingProteomeRNA SplicingRNA, MessengerSignal TransductionTranscription, GeneticTwo-Hybrid System TechniquesConceptsProtein interaction mapsDrosophila melanogasterDraft mapComplementary DNA librarySystems biology modelingHigh-confidence mapMulticellular organismsFly proteomeMultiprotein complexesDNA libraryInteraction mapPathway componentsLevels of organizationKnown pathwaysExtended pathwaysHuman biologyMelanogasterModel systemProteinPathwayProteomeComputational methodsTranscriptsOrganismsBiology