Search results for "microarray."

showing 10 items of 384 documents

Solution Processed Micro- and Nano-Bioarrays for Multiplexed Biosensing

2012

This Feature article reports on solution dispensing methodologies which enable the realization of multiplexed arrays at the micro- and nanoscale for relevant biosensing applications such as drug screening or cellular chips.

ChemistryNanotechnologyBiosensing TechniquesElectrochemical TechniquesEquipment DesignHardware_PERFORMANCEANDRELIABILITYMicroarray AnalysisMultiplexingHigh-Throughput Screening AssaysAnalytical ChemistrySolution processedNano-Hardware_INTEGRATEDCIRCUITSAnimalsHumansNanotechnologyBiochipBiosensorAnalytical Chemistry
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Cascades of transcriptional induction during dendritic cell maturation revealed by genome-wide expression analysis.

2003

Dendritic cells (DC) are central regulators of immunity. Signal-induced maturation of DCs is assumed to be the starting point for specific immune responses. To further understand this process, we analyzed the alteration of transcript profiles along the time course of CD40 ligand-induced maturation of human myeloid DCs by Affymetrix GeneChip microarrays covering >6800 genes. Besides rediscovery of genes already described as associated with DC maturation proving reliability of the methods used, we identified clusterin as novel maturation marker. Looking across the time course, we observed synchronized kinetics of distinct functional groups of molecules whose temporal coregulation underscores …

ChemokineTime FactorsMicroarrayTranscription GeneticCell Survivalmedicine.medical_treatmentImmunoglobulinsBiochemistryMiceAntigens CDGeneticsmedicineAnimalsHumansMolecular BiologyGeneCells CulturedOligonucleotide Array Sequence AnalysisMembrane GlycoproteinsClusterinbiologyGenome HumanReverse Transcriptase Polymerase Chain ReactionGene Expression ProfilingDendritic cell3T3 CellsDendritic CellsFlow CytometryMolecular biologyCell biologyGene expression profilingCytokinebiology.proteinB7-1 AntigenRNAB7-2 AntigenDNA microarrayBiotechnologyFASEB journal : official publication of the Federation of American Societies for Experimental Biology
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MicroRNA Profile in Response to Doxorubicin Treatment in Breast Cancer

2015

UNLABELLED Chemotherapy treatment is the standard in triple negative breast cancers, a cancer subgroup which lacks a specific target. The mechanisms leading to the response, as well as any markers that allow the differentiation between responder and non-responder groups prior to treatment are unknown. In parallel, miRNAs can act as oncogenes or tumor suppressors and there is evidence of their involvement in promoting resistance to anticancer drugs. Therefore we hypothesized that changes in miRNA expression after doxorubicin treatment may also be relevant in treatment response. OBJECTIVE To study miRNAs that are differentially expressed in response to doxorubicin treatment. METHODS One lumin…

ChemotherapyMicroarray analysis techniquesmedicine.medical_treatmentCancerCell BiologyBiologyBioinformaticsmedicine.diseaseBiochemistryBreast cancermicroRNACancer researchmedicineDoxorubicinViability assayMolecular BiologyGenemedicine.drugJournal of Cellular Biochemistry
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Response to long-term NaHCO3-derived alkalinity in model Lotus japonicus Ecotypes Gifu B-129 and Miyakojima MG-20: transcriptomic profiling and physi…

2014

The current knowledge regarding transcriptomic changes induced by alkalinity on plants is scarce and limited to studieswhere plants were subjected to the alkaline salt for periods not longer than 48 h, so there is no information availableregarding the regulation of genes involved in the generation of a new homeostatic cellular condition after long-termalkaline stress.Lotus japonicusis a model legume broadly used to study many important physiological processes includingbiotic interactions and biotic and abiotic stresses. In the present study, we characterized phenotipically the response toalkaline stress of the most widely usedL. japonicusecotypes, Gifu B-129 and MG-20, and analyzed global t…

ChlorophyllOtras Biotecnología AgropecuariaPhysiologyApplied MicrobiologyPlant SciencePathogenesisPathology and Laboratory MedicinePlant RootsBiochemistryTranscriptomeZINCchemistry.chemical_compoundPlant MicrobiologyGene Expression Regulation PlantABIOTIC STRESSMETAL TRANSPORTERSMedicine and Health SciencesOligonucleotide Array Sequence AnalysisLOTUS JAPONICUSPlant Growth and DevelopmentMultidisciplinarybiologyEcotypePlant BiochemistryIRONQRMicrobial Growth and Development//purl.org/becyt/ford/4.4 [https]food and beveragesPlantsZincPlant PhysiologyShootHost-Pathogen InteractionsMedicineAntacidsAnatomymicroarrayPlant ShootsResearch ArticleBiotechnologyHistologyScienceIronPlant Cell BiologyLotus japonicusBiotecnología AgropecuariaalkalinityMycologyReal-Time Polymerase Chain ReactionResearch and Analysis MethodsMicrobiologyModel OrganismsIsoflavonoidSpecies SpecificityPlant and Algal ModelsBotanyAbiotic stressGene Expression ProfilingfungiOrganismsFungiBiology and Life SciencesPlant TranspirationCell Biologybiology.organism_classificationMICROARRAYSGene expression profilingSodium BicarbonatechemistryCIENCIAS AGRÍCOLASChlorophyllLotusPhysiological Processes//purl.org/becyt/ford/4 [https]Developmental BiologyPloS one
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RIP-Chip analysis supports different roles for AGO2 and GW182 proteins in recruiting and processing microRNA targets.

2019

Background MicroRNAs (miRNAs) are small non-coding RNA molecules mediating the translational repression and degradation of target mRNAs in the cell. Mature miRNAs are used as a template by the RNA-induced silencing complex (RISC) to recognize the complementary mRNAs to be regulated. To discern further RISC functions, we analyzed the activities of two RISC proteins, AGO2 and GW182, in the MCF-7 human breast cancer cell line. Methods We performed three RIP-Chip experiments using either anti-AGO2 or anti-GW182 antibodies and compiled a data set made up of the miRNA and mRNA expression profiles of three samples for each experiment. Specifically, we analyzed the input sample, the immunoprecipita…

Chromatin ImmunoprecipitationSupport Vector MachineRIP-Chip data analysisMiRNA bindingComputational biologyBiologylcsh:Computer applications to medicine. Medical informaticsBiochemistryAutoantigens03 medical and health sciencesOpen Reading Frames0302 clinical medicineStructural BiologymicroRNARIP-Chip data analysiCoding regionGene silencingHumansRNA MessengerMolecular BiologyGenelcsh:QH301-705.5030304 developmental biology0303 health sciencesBinding SitesApplied MathematicsGene Expression ProfilingResearchRNARNA-Binding ProteinsmicroRNA target predictionRISC proteins AGO2 and GW182Computer Science ApplicationsSettore BIO/18 - GeneticaMicroRNAslcsh:Biology (General)Gene Expression Regulation030220 oncology & carcinogenesismicroRNA regulatory activityArgonaute ProteinsMCF-7 Cellslcsh:R858-859.7DNA microarrayRIP-ChipBMC bioinformatics
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Topological structure analysis of chromatin interaction networks.

2019

Abstract Background Current Hi-C technologies for chromosome conformation capture allow to understand a broad spectrum of functional interactions between genome elements. Although significant progress has been made into analysis of Hi-C data to identify biologically significant features, many questions still remain open, in particular regarding potential biological significance of various topological features that are characteristic for chromatin interaction networks. Results It has been previously observed that promoter capture Hi-C (PCHi-C) interaction networks tend to separate easily into well-defined connected components that can be related to certain biological functionality, however, …

Chromatin interaction networksFunctionally related modulesComputer scienceCellStructure (category theory)Topologylcsh:Computer applications to medicine. Medical informaticsBiochemistryGenomeChromosome conformation capture03 medical and health sciences0302 clinical medicineGraph topologyStructural BiologyComponent (UML)medicineHumansGene Regulatory NetworksCell type specificityPromoter Regions GeneticMolecular Biologylcsh:QH301-705.5030304 developmental biologyConnected component0303 health sciencesApplied MathematicsResearchChromatinComputer Science ApplicationsChromatinHematopoiesisIdentification (information)medicine.anatomical_structurelcsh:Biology (General)Gene Expression RegulationTopological graph theorylcsh:R858-859.7DNA microarray030217 neurology & neurosurgeryAlgorithmsBMC bioinformatics
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SMART: Unique splitting-while-merging framework for gene clustering

2014

© 2014 Fa et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Successful clustering algorithms are highly dependent on parameter settings. The clustering performance degrades significantly unless parameters are properly set, and yet, it is difficult to set these parameters a priori. To address this issue, in this paper, we propose a unique splitting-while-merging clustering framework, named "splitting merging awareness tactics" (SMART), which does not require any a priori knowledge of either the number …

Clustering algorithmsMicroarrayslcsh:MedicineGene ExpressionBioinformaticscomputer.software_genreCell SignalingData MiningCluster Analysislcsh:ScienceFinite mixture modelOligonucleotide Array Sequence AnalysisPhysicsMultidisciplinarySMART frameworkConstrained clusteringCompetitive learning modelBioassays and Physiological AnalysisMultigene FamilyCanopy clustering algorithmEngineering and TechnologyData miningInformation TechnologyGenomic Signal ProcessingAlgorithmsResearch ArticleSignal TransductionComputer and Information SciencesFuzzy clusteringCorrelation clusteringResearch and Analysis MethodsClusteringMolecular GeneticsCURE data clustering algorithmGeneticsGene RegulationCluster analysista113Gene Expression Profilinglcsh:RBiology and Life SciencesComputational BiologyCell BiologyDetermining the number of clusters in a data setComputingMethodologies_PATTERNRECOGNITIONSplitting-merging awareness tactics (SMART)Signal ProcessingAffinity propagationlcsh:QGene expressionClustering frameworkcomputer
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Computation Cluster Validation in the Big Data Era

2017

Data-driven class discovery, i.e., the inference of cluster structure in a dataset, is a fundamental task in Data Analysis, in particular for the Life Sciences. We provide a tutorial on the most common approaches used for that task, focusing on methodologies for the prediction of the number of clusters in a dataset. Although the methods that we present are general in terms of the data for which they can be used, we offer a case study relevant for Microarray Data Analysis.

Clustering high-dimensional dataClass (computer programming)Clustering validation measureSettore INF/01 - InformaticaComputer sciencebusiness.industryBig dataInferenceMicroarrays data analysiscomputer.software_genreGap statisticTask (project management)ComputingMethodologies_PATTERNRECOGNITIONCURE data clustering algorithmConsensus clusteringHypothesis testing in statisticClustering Class Discovery in Data Algorithmsb Clustering algorithmFigure of meritConsensus clusteringData miningCluster analysisbusinesscomputer
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JANE: efficient mapping of prokaryotic ESTs and variable length sequence reads on related template genomes

2009

Abstract Background ESTs or variable sequence reads can be available in prokaryotic studies well before a complete genome is known. Use cases include (i) transcriptome studies or (ii) single cell sequencing of bacteria. Without suitable software their further analysis and mapping would have to await finalization of the corresponding genome. Results The tool JANE rapidly maps ESTs or variable sequence reads in prokaryotic sequencing and transcriptome efforts to related template genomes. It provides an easy-to-use graphics interface for information retrieval and a toolkit for EST or nucleotide sequence function prediction. Furthermore, we developed for rapid mapping an enhanced sequence align…

Computational biologyBiologylcsh:Computer applications to medicine. Medical informaticsBiochemistryGenomeUser-Computer InterfaceStructural BiologyDatabases Geneticlcsh:QH301-705.5Molecular BiologySequence (medicine)Expressed Sequence TagsWhole genome sequencingGeneticsInternetExpressed sequence tagGenomeBase SequencePhylumApplied MathematicsNucleic acid sequenceComputational BiologySequence Analysis DNAComputer Science Applicationslcsh:Biology (General)Single cell sequencinglcsh:R858-859.7DNA microarraySoftwareBMC Bioinformatics
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CORENup: a combination of convolutional and recurrent deep neural networks for nucleosome positioning identification

2020

Abstract Background Nucleosomes wrap the DNA into the nucleus of the Eukaryote cell and regulate its transcription phase. Several studies indicate that nucleosomes are determined by the combined effects of several factors, including DNA sequence organization. Interestingly, the identification of nucleosomes on a genomic scale has been successfully performed by computational methods using DNA sequence as input data. Results In this work, we propose CORENup, a deep learning model for nucleosome identification. CORENup processes a DNA sequence as input using one-hot representation and combines in a parallel fashion a fully convolutional neural network and a recurrent layer. These two parallel …

Computer scienceCelllcsh:Computer applications to medicine. Medical informaticsBiochemistryConvolutional neural networkDNA sequencingchemistry.chemical_compoundStructural BiologyTranscription (biology)medicineHumansNucleosomeA-DNAEpigeneticsMolecular Biologylcsh:QH301-705.5Nucleosome classificationSettore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniSettore INF/01 - Informaticabiologybusiness.industryApplied MathematicsDeep learningResearchEpigeneticPattern recognitionGenomicsbiology.organism_classificationNucleosomesComputer Science ApplicationsRecurrent neural networkmedicine.anatomical_structurechemistrylcsh:Biology (General)Recurrent neural networkslcsh:R858-859.7Deep learning networksEukaryoteNeural Networks ComputerArtificial intelligenceDNA microarraybusinessDNABMC Bioinformatics
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