2022
Logarithmically scaled, gamma distributed neuronal spiking
Levenstein D, Okun M. Logarithmically scaled, gamma distributed neuronal spiking. The Journal Of Physiology 2022, 601: 3055-3069. PMID: 36086892, PMCID: PMC10952267, DOI: 10.1113/jp282758.Peer-Reviewed Original ResearchConceptsNeuronal spikingFiring rateRate neuronsNon-intuitive propertiesGround stateComputational advantagesForebrain neuronsInterspike intervalsNeuronal populationsDiscrete modesProbability distributionNervous systemNeuronsNeural circuitsOperating regimesGamma distributionAnalytical probability distributionGammaDataOverabundanceSpikesEnhancing cosinor analysis of circadian phase markers using the gamma distribution
Doyle MM, Murphy TE, Miner B, Pisani MA, Lusczek ER, Knauert MP. Enhancing cosinor analysis of circadian phase markers using the gamma distribution. Sleep Medicine 2022, 92: 1-3. PMID: 35306404, PMCID: PMC9356385, DOI: 10.1016/j.sleep.2022.01.015.Peer-Reviewed Original Research
2016
Faster permutation inference in brain imaging
Winkler AM, Ridgway GR, Douaud G, Nichols TE, Smith SM. Faster permutation inference in brain imaging. NeuroImage 2016, 141: 502-516. PMID: 27288322, PMCID: PMC5035139, DOI: 10.1016/j.neuroimage.2016.05.068.Peer-Reviewed Original ResearchConceptsGamma distributionPermutation distributionProperties of statisticsReal data exampleInexpensive computing powerLinear modelGeneralised Pareto distributionMatrix theoryTail approximationNegative binomial distributionSymmetric errorsPareto distributionDirect fittingFamily-wise error ratePermutation inferenceReference resultsComplex modelsBinomial distributionReal dataSynthetic dataExact error rateComputing powerGeneral linear modelFamilywise errorNull hypothesis
2013
Differential expression analysis for paired RNA-seq data
Chung LM, Ferguson JP, Zheng W, Qian F, Bruno V, Montgomery RR, Zhao H. Differential expression analysis for paired RNA-seq data. BMC Bioinformatics 2013, 14: 110. PMID: 23530607, PMCID: PMC3663822, DOI: 10.1186/1471-2105-14-110.Peer-Reviewed Original Research
2008
Modeling ChIP Sequencing In Silico with Applications
Zhang ZD, Rozowsky J, Snyder M, Chang J, Gerstein M. Modeling ChIP Sequencing In Silico with Applications. PLOS Computational Biology 2008, 4: e1000158. PMID: 18725927, PMCID: PMC2507756, DOI: 10.1371/journal.pcbi.1000158.Peer-Reviewed Original ResearchConceptsInitial power-law distributionPower-law distributionBackground genomic sequencesLong right tailStatistical natureGamma distributionComputational foundationRight tailHigh-throughput dataComputational methodsRigorous fashionRecent experimentsChIP-seq dataNonuniform distributionNew methodDistributionActual binding sitesFunctional genomicsGenomewide mappingChIP sequencingSequence tagsChIP-seqTag countsGenomic sequencesGenomic background
2006
Hidden Stochastic Nature of a Single Bacterial Motor
Korobkova EA, Emonet T, Park H, Cluzel P. Hidden Stochastic Nature of a Single Bacterial Motor. Physical Review Letters 2006, 96: 058105. PMID: 16486999, DOI: 10.1103/physrevlett.96.058105.Peer-Reviewed Original Research
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