Search results for " network"

showing 10 items of 6428 documents

Recurrent Deep Neural Networks for Nucleosome Classification

2020

Nucleosomes are the fundamental repeating unit of chromatin. A nucleosome is an 8 histone proteins complex, in which approximately 147–150 pairs of DNA bases bind. Several biological studies have clearly stated that the regulation of cell type-specific gene activities are influenced by nucleosome positioning. Bioinformatic studies have improved those results showing proof of sequence specificity in nucleosomes’ DNA fragment. In this work, we present a recurrent neural network that uses nucleosome sequence features representation for their classification. In particular, we implement an architecture which stacks convolutional and long short-term memory layers, with the main purpose to avoid t…

0301 basic medicineSettore ING-INF/05 - Sistemi Di Elaborazione Delle InformazionibiologySettore INF/01 - InformaticaComputer scienceComputational biologyChromatin03 medical and health scienceschemistry.chemical_compound030104 developmental biologyHistoneRecurrent neural networkchemistryFragment (logic)biology.proteinNucleosomeNucleosome classification Epigenetic Deep learning networks Recurrent Neural NetworksGeneDNASequence (medicine)
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Retrieving infinite numbers of patterns in a spin-glass model of immune networks

2013

The similarity between neural and immune networks has been known for decades, but so far we did not understand the mechanism that allows the immune system, unlike associative neural networks, to recall and execute a large number of memorized defense strategies {\em in parallel}. The explanation turns out to lie in the network topology. Neurons interact typically with a large number of other neurons, whereas interactions among lymphocytes in immune networks are very specific, and described by graphs with finite connectivity. In this paper we use replica techniques to solve a statistical mechanical immune network model with `coordinator branches' (T-cells) and `effector branches' (B-cells), a…

0301 basic medicineSimilarity (geometry)Spin glassComputer sciencestatistical mechanicFOS: Physical sciencesGeneral Physics and AstronomyNetwork topologyTopology01 natural sciencesQuantitative Biology::Cell Behavior03 medical and health sciencesCell Behavior (q-bio.CB)0103 physical sciencesattractor neural-networks; statistical mechanics; brain networks; Physics and Astronomy (all)Physics - Biological Physics010306 general physicsAssociative propertybrain networkArtificial neural networkMechanism (biology)ErgodicityDisordered Systems and Neural Networks (cond-mat.dis-nn)Condensed Matter - Disordered Systems and Neural NetworksAcquired immune system030104 developmental biologyBiological Physics (physics.bio-ph)FOS: Biological sciencesattractor neural-networkQuantitative Biology - Cell Behavior
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Identification of microRNAS differentially regulated by water deficit in relation to mycorrhizal treatment in wheat.

2019

Arbuscular mycorrhizal fungi (AMF) are soil microrganisms that establish symbiosis with plants positively influencing their resistance to abiotic stresses. The aim of this work was to identify wheat miRNAs differentially regulated by water deficit conditions in presence or absence of AMF treatment. Small RNA libraries were constructed for both leaf and root tissues considering four conditions: control (irrigated) or water deficit in presence/absence of mycorrhizal (AMF) treatment. A total of 12 miRNAs were significantly regulated by water deficit in leaves: five in absence and seven in presence of AMF treatment. In roots, three miRNAs were water deficit-modulated in absence of mycorrhizal t…

0301 basic medicineSmall RNABiologyPlant Roots03 medical and health sciences0302 clinical medicineSymbiosisTranscription (biology)Gene Expression Regulation PlantStress PhysiologicalMycorrhizaeBotanymicroRNAGeneticsProtein biosynthesisTranscriptional regulationGene Regulatory NetworksMolecular BiologyDurum wheatWater deficitTriticummiRNAPlant ProteinsAbiotic componentGene Expression ProfilingfungiGene Expression Regulation DevelopmentalGeneral MedicineCell redox homeostasisDroughtsPlant LeavesMicroRNAs030104 developmental biologyRootRNA Plant030220 oncology & carcinogenesisWheatMolecular biology reports
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A multilevel statistical toolkit to study animal social networks: Animal Network Toolkit (ANT) R package

2018

AbstractHow animals interact and develop social relationships regarding, individual attributes, sociodemographic and ecological pressures is of great interest. New methodologies, in particular Social Network Analysis, allow us to elucidate these types of questions. However, the different methodologies developed to that end and the speed at which they emerge make their use difficult. Moreover, the lack of communication between the different software developed to provide an answer to the same/different research questions is a source of confusion. The R package Animal Network Toolkit (ANT) was developed with the aim of implementing in one package the many different social network analysis tech…

0301 basic medicineSocial networkbusiness.industryComputer scienceData science03 medical and health sciencesR package030104 developmental biology0302 clinical medicine[SDE]Environmental SciencesSocial relationshipbusinessSocial network analysis030217 neurology & neurosurgery
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Analyzing the feasibility of time correlated spectral entropy for the assessment of neuronal synchrony

2016

In this paper, we study neuronal network analysis based on microelectrode measurements. We search for potential relations between time correlated changes in spectral distributions and synchrony for neuronal network activity. Spectral distribution is quantified by spectral entropy as a measure of uniformity/complexity and this measure is calculated as a function of time for the recorded neuronal signals, i.e., time variant spectral entropy. Time variant correlations in the spectral distributions between different parts of a neuronal network, i.e., of concurrent measurements via different microelectrodes, are calculated to express the relation with a single scalar. We demonstrate these relati…

0301 basic medicineSpectral power distributionhippocampusta3112Correlation03 medical and health sciences0302 clinical medicineStatisticsBiological neural networkAnimalsEntropy (information theory)Neuronal synchronyAnalysis methodMathematicsta217Quantitative Biology::Neurons and Cognitionta213Spectral entropybiological neural networkselectrodesrats030104 developmental biologycorrelationBiological systementropyprobesMicroelectrodes030217 neurology & neurosurgery
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Multiple steady states and the form of response functions to antigen in a model for the initiation of T cell activation

2017

The aim of this paper is to study the qualitative behaviour predicted by a mathematical model for the initial stage of T-cell activation. The state variables in the model are the concentrations of phosphorylation states of the T-cell receptor (TCR) complex and the phosphatase SHP-1 in the cell. It is shown that these quantities cannot approach zero and that the model possesses more than one positive steady state for certain values of the parameters. It can also exhibit damped oscillations. It is proved that the chemical concentration which represents the degree of activation of the cell, that of the maximally phosphorylated form of the TCR complex, is, in general, a non-monotone function of…

0301 basic medicineState variable1004T cellMolecular Networks (q-bio.MN)PhosphatasemultistationarityDynamical Systems (math.DS)24Dissociation (chemistry)immunology03 medical and health sciences119medicineFOS: Mathematics1008Quantitative Biology - Molecular NetworksMathematics - Dynamical Systemslcsh:ScienceReceptort cellsMultidisciplinaryChemistryT-cell receptor92C37Dissociation constant030104 developmental biologymedicine.anatomical_structureFOS: Biological sciencesBiophysicsPhosphorylationlcsh:QMathematicsResearch Article
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Partitioned learning of deep Boltzmann machines for SNP data.

2016

Abstract Motivation Learning the joint distributions of measurements, and in particular identification of an appropriate low-dimensional manifold, has been found to be a powerful ingredient of deep leaning approaches. Yet, such approaches have hardly been applied to single nucleotide polymorphism (SNP) data, probably due to the high number of features typically exceeding the number of studied individuals. Results After a brief overview of how deep Boltzmann machines (DBMs), a deep learning approach, can be adapted to SNP data in principle, we specifically present a way to alleviate the dimensionality problem by partitioned learning. We propose a sparse regression approach to coarsely screen…

0301 basic medicineStatistics and ProbabilityComputer scienceMachine learningcomputer.software_genre01 natural sciencesBiochemistryPolymorphism Single NucleotideMachine Learning010104 statistics & probability03 medical and health sciencessymbols.namesakeJoint probability distributionHumans0101 mathematicsMolecular BiologyStatistical hypothesis testingArtificial neural networkbusiness.industryGene Expression Regulation LeukemicDeep learningUnivariateComputational BiologyManifoldComputer Science ApplicationsData setComputational Mathematics030104 developmental biologyComputingMethodologies_PATTERNRECOGNITIONComputational Theory and MathematicsLeukemia MyeloidBoltzmann constantsymbolsData miningArtificial intelligencebusinesscomputerSoftwareCurse of dimensionalityBioinformatics (Oxford, England)
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Reactome diagram viewer: data structures and strategies to boost performance

2017

Abstract Motivation Reactome is a free, open-source, open-data, curated and peer-reviewed knowledgebase of biomolecular pathways. For web-based pathway visualization, Reactome uses a custom pathway diagram viewer that has been evolved over the past years. Here, we present comprehensive enhancements in usability and performance based on extensive usability testing sessions and technology developments, aiming to optimize the viewer towards the needs of the community. Results The pathway diagram viewer version 3 achieves consistently better performance, loading and rendering of 97% of the diagrams in Reactome in less than 1 s. Combining the multi-layer html5 canvas strategy with a space partit…

0301 basic medicineStatistics and ProbabilityDatabases FactualComputer scienceKnowledge BasesDatabases and OntologiesBiochemistryWorld Wide Web03 medical and health sciences0302 clinical medicineHumansMolecular BiologyInternetComputational BiologyData structureOriginal PapersComputer Science ApplicationsVisualizationComputational Mathematics030104 developmental biologyComputational Theory and Mathematics030220 oncology & carcinogenesisScalabilityAlgorithmsMetabolic Networks and PathwaysSoftwareBioinformatics
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Gene-based and semantic structure of the Gene Ontology as a complex network

2012

The last decade has seen the advent and consolidation of ontology based tools for the identification and biological interpretation of classes of genes, such as the Gene Ontology. The information accumulated time-by-time and included in the GO is encoded in the definition of terms and in the setting up of semantic relations amongst terms. This approach might be usefully complemented by a bottom-up approach based on the knowledge of relationships amongst genes. To this end, we investigate the Gene Ontology from a complex network perspective. We consider the semantic network of terms naturally associated with the semantic relationships provided by the Gene Ontology consortium and a gene-based …

0301 basic medicineStatistics and ProbabilityFOS: Computer and information sciencesPhysics - Physics and SocietyComplex systemComputer scienceMolecular Networks (q-bio.MN)Complex systemFOS: Physical sciencesNetworkCondensed Matter PhysicPhysics and Society (physics.soc-ph)computer.software_genreQuantitative Biology - Quantitative MethodsStatistics - ApplicationsGeneSemantic network03 medical and health sciencesSemantic similarityQuantitative Biology - Molecular NetworksApplications (stat.AP)GeneQuantitative Methods (q-bio.QM)Community detectionGene ontologybusiness.industryOntologyOntology-based data integrationComplex networkCondensed Matter PhysicsBipartite system030104 developmental biologyBipartite system; Community detection; Complex systems; Genes; Networks; Ontology; Condensed Matter Physics; Statistics and ProbabilityFOS: Biological sciencesOntologyWeighted networkData miningArtificial intelligenceComputingMethodologies_GENERALbusinesscomputerNatural language processing
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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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