Search results for "Applied Mathematics"

showing 10 items of 4379 documents

HPG pore: an efficient and scalable framework for nanopore sequencing data.

2016

The use of nanopore technologies is expected to spread in the future because they are portable and can sequence long fragments of DNA molecules without prior amplification. The first nanopore sequencer available, the MinION™ from Oxford Nanopore Technologies, is a USB-connected, portable device that allows real-time DNA analysis. In addition, other new instruments are expected to be released soon, which promise to outperform the current short-read technologies in terms of throughput. Despite the flood of data expected from this technology, the data analysis solutions currently available are only designed to manage small projects and are not scalable. Here we present HPG Pore, a toolkit for …

0301 basic medicineComputer scienceApplied MathematicsDistributed computingDNASequence Analysis DNAData scienceBiochemistryComputer Science Applications03 medical and health scienceschemistry.chemical_compoundNanoporeNanopores030104 developmental biology0302 clinical medicinechemistryStructural Biology030220 oncology & carcinogenesisScalabilityNanopore sequencingDNA microarrayThroughput (business)Molecular BiologyDNASoftwareBMC bioinformatics
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Deep learning architectures for prediction of nucleosome positioning from sequences data

2018

Abstract Background Nucleosomes are DNA-histone complex, each wrapping about 150 pairs of double-stranded DNA. Their function is fundamental for one of the primary functions of Chromatin i.e. packing the DNA into the nucleus of the Eukaryote cells. Several biological studies have shown that the nucleosome positioning influences the regulation of cell type-specific gene activities. Moreover, computational studies have shown evidence of sequence specificity concerning the DNA fragment wrapped into nucleosomes, clearly underlined by the organization of particular DNA substrings. As the main consequence, the identification of nucleosomes on a genomic scale has been successfully performed by com…

0301 basic medicineComputer scienceCellBiochemistrychemistry.chemical_compound0302 clinical medicineStructural Biologylcsh:QH301-705.5Nucleosome classificationSequenceSettore INF/01 - InformaticabiologyApplied MathematicsEpigeneticComputer Science ApplicationsChromatinNucleosomesmedicine.anatomical_structurelcsh:R858-859.7EukaryoteDNA microarrayDatabases Nucleic AcidComputational biologySaccharomyces cerevisiaelcsh:Computer applications to medicine. Medical informatics03 medical and health sciencesDeep LearningmedicineNucleosomeAnimalsHumansEpigeneticsMolecular BiologyGeneBase Sequencebusiness.industryDeep learningResearchReproducibility of Resultsbiology.organism_classificationYeastNucleosome classification Epigenetic Deep learning networks Recurrent neural networks030104 developmental biologylcsh:Biology (General)chemistryRecurrent neural networksROC CurveDeep learning networksArtificial intelligenceNeural Networks Computerbusiness030217 neurology & neurosurgeryDNABMC Bioinformatics
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A new parallel pipeline for DNA methylation analysis of long reads datasets

2017

Background DNA methylation is an important mechanism of epigenetic regulation in development and disease. New generation sequencers allow genome-wide measurements of the methylation status by reading short stretches of the DNA sequence (Methyl-seq). Several software tools for methylation analysis have been proposed over recent years. However, the current trend is that the new sequencers and the ones expected for an upcoming future yield sequences of increasing length, making these software tools inefficient and obsolete. Results In this paper, we propose a new software based on a strategy for methylation analysis of Methyl-seq sequencing data that requires much shorter execution times while…

0301 basic medicineComputer scienceParallel pipelineADN02 engineering and technologycomputer.software_genreBiochemistrySensitivity and SpecificityDNA sequencingEpigenesis Genetic03 medical and health scienceschemistry.chemical_compoundStructural BiologyRNA analysisInformàticaDatabases Genetic0202 electrical engineering electronic engineering information engineeringHumansEpigeneticsMolecular Biology020203 distributed computingDNA methylationGenome HumanApplied MathematicsParallel pipelineMethylationSequence Analysis DNASupercomputerComputer Science ApplicationsGenòmica030104 developmental biologychemistryGene Expression RegulationDNA methylationMutationData miningHigh performance computingDNA microarraycomputerSequence AlignmentDNASoftware
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Remarks on GRN-type systems

2020

Systems of ordinary differential equations that appear in gene regulatory networks theory are considered. We are focused on asymptotical behavior of solutions. There are stable critical points as well as attractive periodic solutions in two-dimensional and three-dimensional systems. Instead of considering multiple parameters (10 in a two-dimensional system) we focus on typical behaviors of nullclines. Conclusions about possible attractors are made.

0301 basic medicineComputer sciencelcsh:RGeneral EngineeringGene regulatory networkattractorslcsh:MedicineType (model theory)Nullcline03 medical and health sciences030104 developmental biology0302 clinical medicineordinary differential equations030220 oncology & carcinogenesisOrdinary differential equationAttractorgenetic regulatory networksApplied mathematicslcsh:Qlcsh:ScienceFocus (optics)4open
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Dynamic large-scale network synchronization from perception to action

2018

Sensory-guided actions entail the processing of sensory information, generation of perceptual decisions, and the generation of appropriate actions. Neuronal activity underlying these processes is distributed into sensory, fronto-parietal, and motor brain areas, respectively. How the neuronal processing is coordinated across these brain areas to support functions from perception to action remains unknown. We investigated whether phase synchronization in large-scale networks coordinate these processes. We recorded human cortical activity with magnetoencephalography (MEG) during a task in which weak somatosensory stimuli remained unperceived or were perceived. We then assessed dynamic evolutio…

0301 basic medicineComputer sciencemedia_common.quotation_subjectSomatosensorySensory systemSynchronizationSomatosensory systemlcsh:RC321-57103 medical and health sciences0302 clinical medicineArtificial IntelligencePerceptionMotor systemSynchronization (computer science)medicinePremovement neuronal activitylcsh:Neurosciences. Biological psychiatry. Neuropsychiatrymedia_commonMEGmedicine.diagnostic_testApplied MathematicsGeneral NeuroscienceResearchCommunication3112 NeurosciencesMagnetoencephalographyPhase synchronizationComputer Science Applications030104 developmental biologyActionPerceptionNeuroscience030217 neurology & neurosurgery
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Reactome pathway analysis: a high-performance in-memory approach

2016

Reactome aims to provide bioinformatics tools for visualisation, interpretation and analysis of pathway knowledge to support basic research, genome analysis, modelling, systems biology and education. Pathway analysis methods have a broad range of applications in physiological and biomedical research; one of the main problems, from the analysis methods performance point of view, is the constantly increasing size of the data samples. Here, we present a new high-performance in-memory implementation of the well-established over-representation analysis method. To achieve the target, the over-representation analysis method is divided in four different steps and, for each of them, specific data st…

0301 basic medicineData structuresDatabases FactualPathway analysisComputer scienceInterface (Java)Systems biologycomputer.software_genreGenomeBiochemistry03 medical and health sciences0302 clinical medicineStructural BiologyNucleic AcidsHumansMolecular BiologyApplied MathematicsComputational BiologyProteinsPathway analysisComputer Science ApplicationsTree (data structure)030104 developmental biology030220 oncology & carcinogenesisGraph (abstract data type)Data miningOver-representation analysiscomputerAlgorithmsSoftwareBMC Bioinformatics
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On the minimal number of singular fibers with non-compact Jacobians for families of curves over P1

2016

Abstract Let f : X → P 1 be a non-isotrivial family of semi-stable curves of genus g ≥ 1 defined over an algebraically closed field k. Denote by s nc the number of the singular fibers whose Jacobians are non-compact. We prove that s nc ≥ 5 if k = C and g ≥ 5 ; we also prove that s nc ≥ 4 if char ( k ) > 0 and the relative Jacobian of f is non-smooth.

0301 basic medicineDiscrete mathematicsPure mathematicsApplied MathematicsGeneral Mathematics010102 general mathematics01 natural sciences03 medical and health sciencessymbols.namesakeMathematics::Algebraic Geometry030104 developmental biologyGenus (mathematics)Jacobian matrix and determinantFamily of curvessymbols0101 mathematicsAlgebraically closed fieldMathematicsJournal de Mathématiques Pures et Appliquées
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Measuring spectrally-resolved information transfer.

2020

Information transfer, measured by transfer entropy, is a key component of distributed computation. It is therefore important to understand the pattern of information transfer in order to unravel the distributed computational algorithms of a system. Since in many natural systems distributed computation is thought to rely on rhythmic processes a frequency resolved measure of information transfer is highly desirable. Here, we present a novel algorithm, and its efficient implementation, to identify separately frequencies sending and receiving information in a network. Our approach relies on the invertible maximum overlap discrete wavelet transform (MODWT) for the creation of surrogate data in t…

0301 basic medicineDiscrete wavelet transformInformation transferComputer scienceEntropyInformation Theory0302 clinical medicineWaveletMathematical and Statistical TechniquesMedicine and Health SciencesBiology (General)Wavelet TransformsTemporal cortexMammalsEcologySystems BiologyApplied MathematicsSimulation and ModelingPhysicsWavelet transformMagnetoencephalographyEukaryotaBrainSignal FilteringComputational Theory and MathematicsModeling and SimulationPhysical SciencesVertebratesThermodynamicsEngineering and TechnologyWavelet transforms ; Algorithms ; Magnetoencephalography ; Information entropy ; Signal filtering ; Ferrets ; Permutation ; EntropyAnatomyAlgorithmInformation EntropyAlgorithmsResearch ArticleComputer and Information SciencesQH301-705.5PermutationWavelet AnalysisPrefrontal CortexResearch and Analysis Methods03 medical and health sciencesCellular and Molecular NeuroscienceGeneticsEntropy (information theory)AnimalsHumansInformation flow (information theory)Molecular BiologyEcology Evolution Behavior and SystematicsDiscrete MathematicsFerretsOrganismsBiology and Life Sciences030104 developmental biologyCombinatoricsSignal ProcessingAmniotesTransfer entropyZoologyMathematical Functions030217 neurology & neurosurgeryMathematicsPLoS computational biology
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Strategies for structuring interdisciplinary education in Systems Biology: an European perspective

2016

Systems Biology is an approach to biology and medicine that has the potential to lead to a better understanding of how biological properties emerge from the interaction of genes, proteins, molecules, cells and organisms. The approach aims at elucidating how these interactions govern biological function by employing experimental data, mathematical models and computational simulations. As Systems Biology is inherently multidisciplinary, education within this field meets numerous hurdles including departmental barriers, availability of all required expertise locally, appropriate teaching material and example curricula. As university education at the Bachelor’s level is traditionally built upon…

0301 basic medicineEngineeringSystems biologymedia_common.quotation_subjectStructuringGeneral Biochemistry Genetics and Molecular BiologyArticleEducation03 medical and health sciences0302 clinical medicineExcellenceMultidisciplinary approachDrug DiscoveryComputingMilieux_COMPUTERSANDEDUCATIONLife ScienceSystems and Synthetic BiologyInnovation/dk/atira/pure/sustainabledevelopmentgoals/industry_innovation_and_infrastructureCurriculummedia_commonVLAGFlexibility (engineering)Systeem en Synthetische BiologieScience & TechnologyManagement sciencebusiness.industry4. EducationApplied MathematicsINF/01 - INFORMATICAGAPGénéralitésSystems Biology Training and education3. Good healthComputer Science Applications030104 developmental biologyAction (philosophy)Modeling and Simulationand InfrastructureSDG 9 - Industry Innovation and InfrastructureMathematical & Computational BiologySystems biologybusinessDisciplineSDG 9 - IndustryLife Sciences & Biomedicine030217 neurology & neurosurgery
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CLOVE: classification of genomic fusions into structural variation events

2017

Background A precise understanding of structural variants (SVs) in DNA is important in the study of cancer and population diversity. Many methods have been designed to identify SVs from DNA sequencing data. However, the problem remains challenging because existing approaches suffer from low sensitivity, precision, and positional accuracy. Furthermore, many existing tools only identify breakpoints, and so not collect related breakpoints and classify them as a particular type of SV. Due to the rapidly increasing usage of high throughput sequencing technologies in this area, there is an urgent need for algorithms that can accurately classify complex genomic rearrangements (involving more than …

0301 basic medicineGenomicsBiologycomputer.software_genrelcsh:Computer applications to medicine. Medical informaticsBiochemistryChromosomesDNA sequencingSet (abstract data type)Structural variationUser-Computer Interface03 medical and health sciencesStructural BiologyEscherichia coliHumansCopy-number variationMolecular Biologylcsh:QH301-705.5InternetMethodology ArticleApplied MathematicsBreakpointGenomic rearrangementsDNAGenomicsStructural variationsComputer Science ApplicationsIdentification (information)030104 developmental biologylcsh:Biology (General)Nucleic Acid ConformationGraph (abstract data type)lcsh:R858-859.7Data miningcomputerAlgorithmsBMC Bioinformatics
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