Search results for "ComputingMethodologies_PATTERNRECOGNITION"

showing 10 items of 296 documents

X!TandemPipeline: a tool to manage sequence redundancy for protein inference and phosphosite identification

2017

X!TandemPipeline is a software designed to perform protein inference and to manage redundancy in the results of phosphosite identification by database search. It provides the minimal list of proteins or phosphosites that are present in a set of samples using grouping algorithms based on the principle of parsimony. Regarding proteins, a two-level classification is performed, where groups gather proteins sharing at least one peptide and subgroups gather proteins that are not distinguishable according to the identified peptides. Regarding phosphosites, an innovative approach based on the concept of phosphoisland is used to gather overlapping phosphopeptides. The graphical interface of X!Tandem…

0106 biological sciences0301 basic medicinePhosphopeptidesProteomicsphosphopeptideComputer sciencecomputer.internet_protocolcomputer.software_genre01 natural sciencesBiochemistrydatabase search03 medical and health sciencesSearch engineUser-Computer InterfaceRedundancy (information theory)SoftwareTandem Mass Spectrometry[ INFO.INFO-BI ] Computer Science [cs]/Bioinformatics [q-bio.QM]HumansDatabase search engineAmino Acid SequenceDatabases ProteinGraphical user interfacemass spectrometrybusiness.industrysoftwareprotein inferenceProteinsGeneral ChemistrybioinformaticsSearch EngineBenchmarking030104 developmental biologyComputingMethodologies_PATTERNRECOGNITIONProtein inferenceData mining[INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM]businesscomputerXMLAlgorithms010606 plant biology & botany
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Drosophila Evolution over Space and Time (DEST) - A New Population Genomics Resource

2021

Abstract Drosophila melanogaster is a leading model in population genetics and genomics, and a growing number of whole-genome datasets from natural populations of this species have been published over the last 20 years. A major challenge is the integration of these disparate datasets, often generated using different sequencing technologies and bioinformatic pipelines, which hampers our ability to address questions about the evolution and population structure of this species. Here we address these issues by developing a bioinformatics pipeline that maps pooled sequencing (Pool-Seq) reads from D. melanogaster to a hologenome consisting of fly and symbiont genomes and estimates allele frequenc…

0106 biological sciences0303 health scienceseducation.field_of_studyPopulationPopulation geneticsGenomicsGenome browserComputational biologyInformation repositoryBiology010603 evolutionary biology01 natural sciencesGenome03 medical and health sciencesComputingMethodologies_PATTERNRECOGNITIONHologenome theory of evolutionEvolutionary dynamicseducation030304 developmental biology
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Drosophila Evolution over Space and Time (DEST) : A New Population Genomics Resource

2021

Drosophila melanogaster is a leading model in population genetics and genomics, and a growing number of whole-genome datasets from natural populations of this species have been published over the last years. A major challenge is the integration of disparate datasets, often generated using different sequencing technologies and bioinformatic pipelines, which hampers our ability to address questions about the evolution of this species. Here we address these issues by developing a bioinformatics pipeline that maps pooled sequencing (Pool-Seq) reads from D. melanogaster to a hologenome consisting of fly and symbiont genomes and estimates allele frequencies using either a heuristic (PoolSNP) or a…

0106 biological sciencesdrosophilia melanogasterdemographyQH301 BiologyadaptationAcademicSubjects/SCI0118001 natural sciencesGene Frequencymedia_common0303 health sciencesEuropean researchbioinformatiikkaGenomics3rd-DASgenomiikkaNew populationResourcesDrosophila melanogaster; SNPs; adaptation; demography; evolution; population genomicsDrosophila melanogasterpopulaatiogenetiikkaChristian ministryCorrigendumSNPsResource (biology)population genomicsEvolutionevoluutioLibrary scienceQH426 GeneticsBiology010603 evolutionary biology03 medical and health sciencesQH301evolutionGeneticsmedia_common.cataloged_instanceAnimalsEuropean unionAdaptationMolecular BiologyQH426Ecology Evolution Behavior and Systematics030304 developmental biologyDemographyAcademicSubjects/SCI01130banaanikärpänenNISGenetics PopulationComputingMethodologies_PATTERNRECOGNITIONResearch councilMCPperimäMetagenomicsPopulation genomics
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Ant Colony Optimisation-Based Classification Using Two-Dimensional Polygons

2016

The application of Ant Colony Optimization to the field of classification has mostly been limited to hybrid approaches which attempt at boosting the performance of existing classifiers (such as Decision Trees and Support Vector Machines (SVM)) — often through guided feature reductions or parameter optimizations.

0209 industrial biotechnologyBoosting (machine learning)business.industryComputer scienceAnt colony optimization algorithmsDecision treePattern recognition02 engineering and technologyAnt colonycomputer.software_genreSwarm intelligenceSupport vector machineComputingMethodologies_PATTERNRECOGNITION020901 industrial engineering & automationKernel method0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingArtificial intelligenceData miningbusinesscomputer
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2D/3D Object Recognition and Categorization Approaches for Robotic Grasping

2017

International audience; Object categorization and manipulation are critical tasks for a robot to operate in the household environment. In this paper, we propose new methods for visual recognition and categorization. We describe 2D object database and 3D point clouds with 2D/3D local descriptors which we quantify with the k-means clustering algorithm for obtaining the Bag of Words (BOW). Moreover, we develop a new global descriptor called VFH-Color that combines the original version of Viewpoint Feature Histogram (VFH) descriptor with the color quantization histogram, thus adding the appearance information that improves the recognition rate. The acquired 2D and 3D features are used for train…

0209 industrial biotechnologyComputer sciencebusiness.industryComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONCognitive neuroscience of visual object recognition[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]02 engineering and technology[ INFO.INFO-CV ] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]Color quantizationDeep belief network[INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]ComputingMethodologies_PATTERNRECOGNITION020901 industrial engineering & automationCategorizationBag-of-words modelHistogram0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingComputer visionArtificial intelligenceCluster analysisbusinessClassifier (UML)
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Predictive Model Markup Language (PMML) Representation of Bayesian Networks: An Application in Manufacturing

2018

International audience; Bayesian networks (BNs) represent a promising approach for the aggregation of multiple uncertainty sources in manufacturing networks and other engineering systems for the purposes of uncertainty quantification, risk analysis, and quality control. A standardized representation for BN models will aid in their communication and exchange across the web. This article presents an extension to the predictive model markup language (PMML) standard for the representation of a BN, which may consist of discrete variables, continuous variables, or their combination. The PMML standard is based on extensible markup language (XML) and used for the representation of analytical models…

0209 industrial biotechnologyDesignComputer sciencecomputer.internet_protocol02 engineering and technologycomputer.software_genreBayesian inferenceIndustrial and Manufacturing EngineeringArticle[SPI]Engineering Sciences [physics]020901 industrial engineering & automationPMML0202 electrical engineering electronic engineering information engineeringanalyticsUncertainty quantificationMonte-Carlouncertaintycomputer.programming_languageParsingBayesian networkInformationSystems_DATABASEMANAGEMENTstandardPython (programming language)XMLComputer Science ApplicationsmanufacturingComputingMethodologies_PATTERNRECOGNITIONBayesian networksControl and Systems EngineeringSurface-RoughnessData analysisPredictive Model Markup Language020201 artificial intelligence & image processingData miningcomputerXML
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George-Veeramani Fuzzy Metrics Revised

2018

In this note, we present an alternative approach to the concept of a fuzzy metric, calling it a revised fuzzy metric. In contrast to the traditional approach to the theory of fuzzy metric spaces which is based on the use of a t-norm, we proceed from a t-conorm in the definition of a revised fuzzy metric. Here, we restrict our study to the case of fuzzy metrics as they are defined by George-Veeramani, however, similar revision can be done also for some other approaches to the concept of a fuzzy metric.

0209 industrial biotechnologyLogicComputer scienceMathematics::General Mathematicst-norm02 engineering and technologyFuzzy logic<i>t</i>-norm020901 industrial engineering & automationGEORGE (programming language)0202 electrical engineering electronic engineering information engineeringt-conormMathematical PhysicsAlgebra and Number Theorybusiness.industrylcsh:MathematicsContrast (statistics)T-normlcsh:QA1-939Fuzzy metric spaceComputingMethodologies_PATTERNRECOGNITIONrestrictMetric (mathematics)<i>t</i>-conormfuzzy metric020201 artificial intelligence & image processingGeometry and TopologyArtificial intelligenceComputingMethodologies_GENERALbusinessAnalysisAxioms
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An Artificial Bee Colony Approach for Classification of Remote Sensing Imagery

2018

This paper presents a novel Artificial Bee Colony (ABC) approach for supervised classification of remote sensing images. One proposes to apply an ABC algorithm to optimize the coefficients of the set of polynomial discriminant functions. We have experimented the proposed ABC-based classifier algorithm for a Landsat 7 ETM+ image database, evaluating the influence of the ABC model parameters on the classifier performances. Such ABC model parameters are: numbers of employed/onlooker/scout bees, number of epochs, and polynomial degree. One has compared the best ABC classifier Overall Accuracy (OA) with the performances obtained using a set of benchmark classifiers (NN, NP, RBF, and SVM). The re…

021103 operations researchArtificial neural networkComputer science0211 other engineering and technologies02 engineering and technologyArtificial bee colony algorithmSupport vector machineStatistical classificationAbc modelComputingMethodologies_PATTERNRECOGNITIONDiscriminant0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingDegree of a polynomialClassifier (UML)Remote sensing2018 10th International Conference on Electronics, Computers and Artificial Intelligence (ECAI)
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Engineering of a DNA Polymerase for Direct m6A Sequencing

2017

Methods for the detection of RNA modifications are of fundamental importance for advancing epitranscriptomics. N6-methyladenosine (m6A) is the most abundant RNA modification in mammalian mRNA and is involved in the regulation of gene expression. Current detection techniques are laborious and rely on antibody-based enrichment of m6A-containing RNA prior to sequencing, since m6A modifications are generally "erased" during reverse transcription (RT). To overcome the drawbacks associated with indirect detection, we aimed to generate novel DNA polymerase variants for direct m6A sequencing. Therefore, we developed a screen to evolve an RT-active KlenTaq DNA polymerase variant that sets a mark for…

0301 basic medicineAdenosineRNA-dependent RNA polymeraseDNA-Directed DNA Polymerase010402 general chemistryProtein Engineering01 natural sciencesCatalysis03 medical and health sciencesDNA polymerasesSequencing by hybridization[SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry Molecular Biology/Genomics [q-bio.GN]TheoryofComputation_ANALYSISOFALGORITHMSANDPROBLEMCOMPLEXITYRNA polymerase IRNA MessengerPolymerasebiologyOligonucleotideN6-methyladenosineReverse Transcriptase Polymerase Chain ReactionCommunicationMultiple displacement amplificationHigh-Throughput Nucleotide Sequencing[SDV.BBM.BM]Life Sciences [q-bio]/Biochemistry Molecular Biology/Molecular biologyGeneral ChemistryDNA MethylationRNA modificationMolecular biologyReverse transcriptaseCommunications0104 chemical sciencesSequencing by ligationenzyme engineering030104 developmental biologyComputingMethodologies_PATTERNRECOGNITIONddc:540biology.proteinepitranscriptomicsRNA Methylation
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CellMap visualizes protein-protein interactions and subcellular localization

2018

Many tools visualize protein-protein interaction (PPI) networks. The tool introduced here, CellMap, adds one crucial novelty by visualizing PPI networks in the context of subcellular localization, i.e. the location in the cell or cellular component in which a PPI happens. Users can upload images of cells and define areas of interest against which PPIs for selected proteins are displayed (by default on a cartoon of a cell). Annotations of localization are provided by the user or through our in-house database. The visualizer and server are written in JavaScript, making CellMap easy to customize and to extend by researchers and developers.

0301 basic medicineBioinformaticssubcellular locationContext (language use)BiologyJavaScriptGeneral Biochemistry Genetics and Molecular BiologyChemical Biology of the CellProtein–protein interactionprotein-protein interaction03 medical and health sciencesUploadHuman–computer interactionGeneral Pharmacology Toxicology and Pharmaceuticscomputer.programming_languagebiological visualization030102 biochemistry & molecular biologyGeneral Immunology and MicrobiologySoftware Tool ArticleNoveltyArticlesGeneral MedicineSubcellular localizationddc:ComputingMethodologies_PATTERNRECOGNITION030104 developmental biologyNeurosciencecomputerF1000Research
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