Search results for " Mathematics"

showing 10 items of 10797 documents

ParDRe: faster parallel duplicated reads removal tool for sequencing studies

2016

This is a pre-copyedited, author-produced version of an article accepted for publication in Bioinformatics following peer review. The version of record [insert complete citation information here] is available online at: https://doi.org/10.1093/bioinformatics/btw038 [Abstract] Summary: Current next generation sequencing technologies often generate duplicated or near-duplicated reads that (depending on the application scenario) do not provide any interesting biological information but can increase memory requirements and computational time of downstream analysis. In this work we present ParDRe , a de novo parallel tool to remove duplicated and near-duplicated reads through the clustering of S…

0301 basic medicineStatistics and ProbabilityFASTQ formatDNA stringsSource codeDownstream (software development)Computer sciencemedia_common.quotation_subjectParallel computingcomputer.software_genreBiochemistryDNA sequencing03 medical and health scienceschemistry.chemical_compound0302 clinical medicineHybrid MPI/multithreadingCluster AnalysisParDReMolecular BiologyGenemedia_commonHigh-Throughput Nucleotide SequencingSequence Analysis DNAParallel toolComputer Science ApplicationsComputational Mathematics030104 developmental biologyComputational Theory and MathematicschemistryData miningcomputerAlgorithms030217 neurology & neurosurgeryDNABioinformatics
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L1-Penalized Censored Gaussian Graphical Model

2018

Graphical lasso is one of the most used estimators for inferring genetic networks. Despite its diffusion, there are several fields in applied research where the limits of detection of modern measurement technologies make the use of this estimator theoretically unfounded, even when the assumption of a multivariate Gaussian distribution is satisfied. Typical examples are data generated by polymerase chain reactions and flow cytometer. The combination of censoring and high-dimensionality make inference of the underlying genetic networks from these data very challenging. In this article, we propose an $\ell_1$-penalized Gaussian graphical model for censored data and derive two EM-like algorithm…

0301 basic medicineStatistics and ProbabilityFOS: Computer and information sciencesgraphical lassoComputer scienceGaussianNormal DistributionInferenceMultivariate normal distribution01 natural sciencesMethodology (stat.ME)010104 statistics & probability03 medical and health sciencessymbols.namesakeGraphical LassoExpectation–maximization algorithmHumansComputer SimulationGene Regulatory NetworksGraphical model0101 mathematicsStatistics - MethodologyEstimation theoryReverse Transcriptase Polymerase Chain ReactionEstimatorexpectation-maximization algorithmGeneral MedicineCensoring (statistics)High-dimensional datahigh-dimensional dataGaussian graphical model030104 developmental biologysymbolscensored dataCensored dataExpectation-Maximization algorithmStatistics Probability and UncertaintySettore SECS-S/01 - StatisticaAlgorithmAlgorithms
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Model selection for factorial Gaussian graphical models with an application to dynamic regulatory networks.

2016

Abstract Factorial Gaussian graphical Models (fGGMs) have recently been proposed for inferring dynamic gene regulatory networks from genomic high-throughput data. In the search for true regulatory relationships amongst the vast space of possible networks, these models allow the imposition of certain restrictions on the dynamic nature of these relationships, such as Markov dependencies of low order – some entries of the precision matrix are a priori zeros – or equal dependency strengths across time lags – some entries of the precision matrix are assumed to be equal. The precision matrix is then estimated by l 1-penalized maximum likelihood, imposing a further constraint on the absolute value…

0301 basic medicineStatistics and ProbabilityFactorialDependency (UML)Computer scienceGaussianNormal Distributionpenalized inferencesparse networkscomputer.software_genreMachine learning01 natural sciencesNormal distribution010104 statistics & probability03 medical and health sciencessymbols.namesakeSparse networksGeneticsComputer SimulationGene Regulatory NetworksGraphical model0101 mathematicsgene-regulatory systemMolecular BiologyProbabilityMarkov chainModels GeneticPenalized inferencebusiness.industryModel selectiongraphical modelGene-regulatory systemsComputational Mathematics030104 developmental biologysymbolsA priori and a posterioriData miningArtificial intelligenceGraphical modelsSettore SECS-S/01 - StatisticabusinesscomputerNeisseriaAlgorithmsStatistical applications in genetics and molecular biology
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Prioritizing covariates in the planning of future studies in the meta-analytic framework

2016

Science can be seen as a sequential process where each new study augments evidence to the existing knowledge. To have the best prospects to make an impact in this process, a new study should be designed optimally taking into account the previous studies and other prior information. We propose a formal approach for the covariate prioritization, i.e., the decision about the covariates to be measured in a new study. The decision criteria can be based on conditional power, change of the p-value, change in lower confidence limit, Kullback-Leibler divergence, Bayes factors, Bayesian false discovery rate or difference between prior and posterior expectation. The criteria can be also used for decis…

0301 basic medicineStatistics and ProbabilityFalse discovery rateComputer scienceBayesian probabilityBayes factorGeneral MedicineMultiple-criteria decision analysis01 natural sciencesConfidence interval010104 statistics & probability03 medical and health sciences030104 developmental biologySample size determinationCovariateEconometrics0101 mathematicsStatistics Probability and UncertaintyDivergence (statistics)Biometrical Journal
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The adaptive value of tandem communication in ants:Insights from an agent-based model

2021

AbstractSocial animals often share information about the location of resources, such as a food source or a new nest-site. One well-studied communication strategy in ants is tandem running, whereby a leader guides a recruit to a resource. Tandem running is considered an example of animal teaching because a leader adjusts her behaviour and invests time to help another ant to learn the location of a resource more efficiently. Tandem running also has costs, such as waiting inside the nest for a leader and a reduced walking speed. Whether and when these costs outweigh the benefits of tandem running is not well understood. We developed an agent-based simulation model to investigate the conditions…

0301 basic medicineStatistics and ProbabilityForage (honey bee)Adaptive valueOperations researchComputer scienceForagingGeneral Biochemistry Genetics and Molecular BiologyRunning03 medical and health sciences0302 clinical medicineResource (project management)NestAnimalsLearningAgent-based modelGeneral Immunology and MicrobiologyTandemAntsCommunicationApplied MathematicsGeneral MedicineBeesVariable (computer science)030104 developmental biologyModeling and SimulationSocial animalFemaleGeneral Agricultural and Biological Sciences030217 neurology & neurosurgeryTandem running
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Identification and visualization of differential isoform expression in RNA-seq time series

2018

Abstract Motivation As sequencing technologies improve their capacity to detect distinct transcripts of the same gene and to address complex experimental designs such as longitudinal studies, there is a need to develop statistical methods for the analysis of isoform expression changes in time series data. Results Iso-maSigPro is a new functionality of the R package maSigPro for transcriptomics time series data analysis. Iso-maSigPro identifies genes with a differential isoform usage across time. The package also includes new clustering and visualization functions that allow grouping of genes with similar expression patterns at the isoform level, as well as those genes with a shift in major …

0301 basic medicineStatistics and ProbabilityGene isoformIdentificationComputer scienceSequence analysisGene ExpressionRNA-SeqComputational biologyBiochemistryBioconductorTranscriptomeMice03 medical and health sciences0302 clinical medicineEstadística e Investigación OperativaRNA IsoformsAnimalsMolecular BiologyGeneVisualizationRegulation of gene expressionB-LymphocytesSequence Analysis RNAGene Expression ProfilingCell DifferentiationApplications NotesComputer Science ApplicationsVisualizationComputational Mathematics030104 developmental biologyGene Expression RegulationComputational Theory and MathematicsRNA-seq time seriesSoftware030217 neurology & neurosurgeryIsoform expression
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A generalization of Kingman's model of selection and mutation and the Lenski experiment.

2017

Kingman’s model of selection and mutation studies the limit type value distribution in an asexual population of discrete generations and infinite size undergoing selection and mutation. This paper generalizes the model to analyze the long-term evolution of Escherichia. coli in Lenski experiment. Weak assumptions for fitness functions are proposed and the mutation mechanism is the same as in Kingman’s model. General macroscopic epistasis are designable through fitness functions. Convergence to the unique limit type distribution is obtained.

0301 basic medicineStatistics and ProbabilityGeneralizationPopulationBiology01 natural sciencesModels BiologicalGeneral Biochemistry Genetics and Molecular Biology010104 statistics & probability03 medical and health sciencesStatisticsEscherichia coliApplied mathematicsQuantitative Biology::Populations and EvolutionLimit (mathematics)0101 mathematicsSelection GeneticeducationSelection (genetic algorithm)education.field_of_studyFitness functionGeneral Immunology and MicrobiologyApplied MathematicsGeneral MedicineQuantitative Biology::GenomicsBiological Evolution030104 developmental biologyDistribution (mathematics)Modeling and SimulationMutation (genetic algorithm)MutationEpistasisGeneral Agricultural and Biological SciencesMathematical biosciences
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Nature lessons: the whitefly bacterial endosymbiont is a minimal amino acid factory with unusual energetics

2016

Reductive genome evolution is a universal phenomenon observed in endosymbiotic bacteria in insects. As the genome reduces its size and irreversibly losses coding genes, the functionalities of the cell system, including the energetics processes, are more restricted. Several energetic pathways can also be lost. How do these reduced metabolic networks sustain the energy needs of the system? Among the bacteria with reduced genomes Candidatus Portiera aleyrodidarum, obligate endosymbiont of whiteflies, represents an extreme case since lacks several key mechanisms for ATP generation. Thus, to analyze the cell energetics in this system, a genome-scale metabolic model of this endosymbiont was const…

0301 basic medicineStatistics and ProbabilityGenome evolutionAnabolismSystems biology030106 microbiologyCell EnergeticsBiologyModels BiologicalGenomeGeneral Biochemistry Genetics and Molecular BiologyHemiptera03 medical and health sciencesMetabolic flux analysisAnimalsAmino AcidsSymbiosisGeneGenome sizeCarotenoidchemistry.chemical_classificationGeneral Immunology and MicrobiologyObligateApplied MathematicsEnergeticsGeneral MedicineMetabolismbeta Carotenebiology.organism_classificationMetabolic Flux AnalysisAmino acidHalomonadaceae030104 developmental biologychemistryBiochemistryModeling and SimulationEnergy MetabolismGeneral Agricultural and Biological SciencesGenome BacterialMetabolic Networks and PathwaysBacteria
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The latent geometry of the human protein interaction network

2017

Abstract Motivation A series of recently introduced algorithms and models advocates for the existence of a hyperbolic geometry underlying the network representation of complex systems. Since the human protein interaction network (hPIN) has a complex architecture, we hypothesized that uncovering its latent geometry could ease challenging problems in systems biology, translating them into measuring distances between proteins. Results We embedded the hPIN to hyperbolic space and found that the inferred coordinates of nodes capture biologically relevant features, like protein age, function and cellular localization. This means that the representation of the hPIN in the two-dimensional hyperboli…

0301 basic medicineStatistics and ProbabilityGeometric analysisComputer scienceHyperbolic geometrySystems biologyComplex systemContext (language use)GeometryBiochemistryProtein–protein interaction03 medical and health sciencesInteraction networkHumansProtein Interaction MapsRepresentation (mathematics)Cluster analysisMolecular BiologySystems BiologyHyperbolic spaceProteinsFunction (mathematics)Original PapersComputer Science ApplicationsComputational Mathematics030104 developmental biologyComputational Theory and MathematicsEmbeddingSignal transductionAlgorithmsSignal Transduction
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panISa: ab initio detection of insertion sequences in bacterial genomes from short read sequence data.

2018

Abstract Motivation The advent of next-generation sequencing has boosted the analysis of bacterial genome evolution. Insertion sequence (IS) elements play a key role in prokaryotic genome organization and evolution, but their repetitions in genomes complicate their detection from short-read data. Results PanISa is a software pipeline that identifies IS insertions ab initio in bacterial genomes from short-read data. It is a highly sensitive and precise tool based on the detection of read-mapping patterns at the insertion site. PanISa performs better than existing IS detection systems as it is based on a database-free approach. We applied it to a high-risk clone lineage of the pathogenic spec…

0301 basic medicineStatistics and ProbabilityLineage (genetic)Computer scienceAb initioComputational biologyBacterial genome size[INFO.INFO-SE]Computer Science [cs]/Software Engineering [cs.SE]BiochemistryGenome[INFO.INFO-IU]Computer Science [cs]/Ubiquitous Computing03 medical and health sciences[INFO.INFO-CR]Computer Science [cs]/Cryptography and Security [cs.CR][SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry Molecular Biology/Genomics [q-bio.GN]Insertion sequenceMolecular BiologyGenomic organizationHigh-Throughput Nucleotide SequencingSequence Analysis DNA[SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM][SDV.MP.BAC]Life Sciences [q-bio]/Microbiology and Parasitology/BacteriologyPipeline (software)[INFO.INFO-MO]Computer Science [cs]/Modeling and SimulationComputer Science ApplicationsComputational Mathematics030104 developmental biologyComputational Theory and Mathematics[INFO.INFO-MA]Computer Science [cs]/Multiagent Systems [cs.MA]DNA Transposable Elements[INFO.INFO-ET]Computer Science [cs]/Emerging Technologies [cs.ET][INFO.INFO-DC]Computer Science [cs]/Distributed Parallel and Cluster Computing [cs.DC]Genome BacterialSoftwareBioinformatics (Oxford, England)
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