2024
Automating life science labs at the single-cell level through precise ultrasonic liquid sample ejection: PULSE
Zhang P, Tian Z, Jin K, Yang K, Collyer W, Rufo J, Upreti N, Dong X, Lee L, Huang T. Automating life science labs at the single-cell level through precise ultrasonic liquid sample ejection: PULSE. Microsystems & Nanoengineering 2024, 10: 172. PMID: 39567484, PMCID: PMC11579414, DOI: 10.1038/s41378-024-00798-y.Peer-Reviewed Original ResearchSingle-cell levelSingle cellsAutomated solutionPreserving cell integrityGenotype dataPhenotypic dataDownstream analysisBarcoding experimentsPrecision gatesBiomedical researchTiter platesBiocompatible mannerLife science labsDeterministic arraysBiological experimentsCell integritySpeed rangeBarcodingPulsePulse platformAutomation technologyDynamic analysisCells
2020
Rational Design of Bioavailable Photosensitizers for Manipulation and Imaging of Biological Systems
Binns TC, Ayala AX, Grimm JB, Tkachuk AN, Castillon GA, Phan S, Zhang L, Brown TA, Liu Z, Adams SR, Ellisman MH, Koyama M, Lavis LD. Rational Design of Bioavailable Photosensitizers for Manipulation and Imaging of Biological Systems. Cell Chemical Biology 2020, 27: 1063-1072.e7. PMID: 32698018, PMCID: PMC7483975, DOI: 10.1016/j.chembiol.2020.07.001.Peer-Reviewed Original ResearchConceptsBiological systemsChemical toolsRational designChemical reactionsPhotosensitizerElectron microscopyChromophore-assisted light inactivationNumerous biological experimentsHigh-resolution imagingPowerful methodPhotopolymerizationReactive oxygen speciesRhodamineOxygen speciesSynthesisTargeted destructionReactionBiological experimentsBroad rangeMicroscopyCharacterizationCell ablationDiaminobenzidine
2018
Prediction of RNA-protein interactions with distributed feature representations and a hybrid deep model
Zhang K, Xiao Y, Pan X, Yang Y. Prediction of RNA-protein interactions with distributed feature representations and a hybrid deep model. 2018, 1-5. DOI: 10.1145/3240876.3240912.Peer-Reviewed Original ResearchPrediction of RNA-protein interactionsRNA sequencingProtein-RNA interactionsRNA-protein interactionsComputational prediction toolsRNA-binding-proteinBiological processesOne-hot vectorDeep learning architectureHybrid deep modelBiological experimentsRNAMachine learning modelsSequenceBenchmark datasetsDeep modelsLearning architectureDistributed representationClassification modelComputational toolsStatistical featuresLearning modelsProteinPrediction toolsPredictive performance
2010
Quantitative Characterization of the Interactions among c-myc Transcriptional Regulators FUSE, FBP, and FIR
Hsiao HH, Nath A, Lin CY, Folta-Stogniew EJ, Rhoades E, Braddock DT. Quantitative Characterization of the Interactions among c-myc Transcriptional Regulators FUSE, FBP, and FIR. Biochemistry 2010, 49: 4620-4634. PMID: 20420426, DOI: 10.1021/bi9021445.Peer-Reviewed Original ResearchMeSH KeywordsAmino Acid SequenceBase SequenceCarrier ProteinsDimerizationDNA HelicasesDNA-Binding ProteinsGuanine Nucleotide Exchange FactorsHumansModels, MolecularMolecular Sequence DataNucleic Acid ConformationProtein BindingProto-Oncogene Proteins c-mycRepressor ProteinsRho Guanine Nucleotide Exchange FactorsRNA Splicing FactorsRNA-Binding ProteinsSolutionsTrans-ActivatorsConceptsDNA strand preferencesProtein-DNA interactionsC-myc transcriptionPotent oncogenic factorHuman c-mycFBP bindsTranscriptional regulationActive transcriptionNear-physiological conditionsTripartite interactionCell homeostasisInhibitory complexStrand preferenceC-MycOncogenic factorRegulatory systemUnique modeTranscriptionStrand DNABiological experimentsComplex formationLow micromolar rangeDNADifferent conformationsMicromolar range
2006
Bayesian error analysis model for reconstructing transcriptional regulatory networks
Sun N, Carroll RJ, Zhao H. Bayesian error analysis model for reconstructing transcriptional regulatory networks. Proceedings Of The National Academy Of Sciences Of The United States Of America 2006, 103: 7988-7993. PMID: 16702552, PMCID: PMC1472417, DOI: 10.1073/pnas.0600164103.Peer-Reviewed Original ResearchConceptsTranscriptional regulatory networksGene expression dataTranscription regulationRegulatory networksExpression dataProtein-DNA binding dataDNA sequence dataFundamental biological processesYeast cell cycleHigh-throughput technologiesMicroarray gene expression dataBiological experimentsSequence dataGenomic dataBiological processesCell cycleClear biological interpretationThroughput technologiesBiological interpretationMarkov chain Monte CarloBayesian hierarchical model frameworkBiochemical reactionsRegulationLinear system modelHierarchical model framework
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