Search results for "GENRE"

showing 10 items of 4351 documents

How to deal with Haplotype data: An Extension to the Conceptual Schema of the Human Genome

2016

[EN] The goal of this work is to describe the advantages of the application of Conceptual Modeling (CM) in complex domains, such as genomics. Nowadays, the study and comprehension of the human genome is a major challenge due to its high level of complexity. The constant evolution in the genomic domain contributes to the generation of ever larger amounts of new data, which means that if we do not manage it correctly data quality could be compromised (i.e., problems related with heterogeneity and inconsistent data). In this paper, we propose the use of a Conceptual Schema of the Human Genome (CSHG), designed to understand and improve our ontological commitment to the domain and also extend (e…

0301 basic medicinehaplotypesGeISHigher education0206 medical engineeringconceptual modelingLibrary science02 engineering and technologystatistical modelscomputer.software_genreGenomelcsh:QA75.5-76.95Conceptual schema03 medical and health sciencesExtension (metaphysics)Sociologybusiness.industryHaplotypeGeneral MedicineConceptual modelingStatistical models030104 developmental biologyHaplotypesGenetic diagnosisChristian ministryHuman genomelcsh:Electronic computers. Computer scienceData miningbusinessGenetic diagnosiscomputer020602 bioinformaticsgenetic diagnosis.
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Methods for RNA Modification Mapping Using Deep Sequencing: Established and New Emerging Technologies

2019

New analytics of post-transcriptional RNA modifications have paved the way for a tremendous upswing of the biological and biomedical research in this field. This especially applies to methods that included RNA-Seq techniques, and which typically result in what is termed global scale modification mapping. In this process, positions inside a cell`s transcriptome are receiving a status of potential modification sites (so called modification calling), typically based on a score of some kind that issues from the particular method applied. The resulting data are thought to represent information that goes beyond what is contained in typical transcriptome data, and hence the field has taken to use …

0301 basic medicinelcsh:QH426-470Computer scienceProcess (engineering)Emerging technologieschemical treatmentNext Generation Sequencingengineered Reverse Transcriptase enzymesRNA-SeqReviewcomputer.software_genreDeep sequencingField (computer science)deep sequencing03 medical and health sciences0302 clinical medicineepitranscriptome[SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry Molecular Biology/Genomics [q-bio.GN]GeneticsAnimalsHumansRNA-SeqRNA Processing Post-TranscriptionalComputingMilieux_MISCELLANEOUSGenetics (clinical)Sequence Analysis RNAbusiness.industryScale (chemistry)High-Throughput Nucleotide Sequencing[SDV.BBM.BM]Life Sciences [q-bio]/Biochemistry Molecular Biology/Molecular biologyRNA modificationTerm (time)lcsh:Genetics030104 developmental biologyAnalyticsRNAData miningbusinesscomputer030217 neurology & neurosurgeryGenes
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Graphical Workflow System for Modification Calling by Machine Learning of Reverse Transcription Signatures

2019

Modification mapping from cDNA data has become a tremendously important approach in epitranscriptomics. So-called reverse transcription signatures in cDNA contain information on the position and nature of their causative RNA modifications. Data mining of, e.g. Illumina-based high-throughput sequencing data, is therefore fast growing in importance, and the field is still lacking effective tools. Here we present a versatile user-friendly graphical workflow system for modification calling based on machine learning. The workflow commences with a principal module for trimming, mapping, and postprocessing. The latter includes a quantification of mismatch and arrest rates with single-nucleotide re…

0301 basic medicinelcsh:QH426-470Downstream (software development)Computer scienceRT signatureMachine learningcomputer.software_genre[SDV.BBM.BM] Life Sciences [q-bio]/Biochemistry Molecular Biology/Molecular biologyField (computer science)m1A03 medical and health sciencesRNA modifications0302 clinical medicineEpitranscriptomics[SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry Molecular Biology/Genomics [q-bio.GN]GeneticsTechnology and CodeGalaxy platformGenetics (clinical)ComputingMilieux_MISCELLANEOUSbusiness.industryPrincipal (computer security)[SDV.BBM.BM]Life Sciences [q-bio]/Biochemistry Molecular Biology/Molecular biologyAutomationWatson–Crick faceVisualizationlcsh:Geneticsmachine learningComputingMethodologies_PATTERNRECOGNITION030104 developmental biologyWorkflow030220 oncology & carcinogenesisMolecular Medicine[SDV.BBM.GTP] Life Sciences [q-bio]/Biochemistry Molecular Biology/Genomics [q-bio.GN]TrimmingArtificial intelligencebusinesscomputer
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Taxonomic Classification for Living Organisms Using Convolutional Neural Networks

2017

Taxonomic classification has a wide-range of applications such as finding out more about evolutionary history. Compared to the estimated number of organisms that nature harbors, humanity does not have a thorough comprehension of to which specific classes they belong. The classification of living organisms can be done in many machine learning techniques. However, in this study, this is performed using convolutional neural networks. Moreover, a DNA encoding technique is incorporated in the algorithm to increase performance and avoid misclassifications. The algorithm proposed outperformed the state of the art algorithms in terms of accuracy and sensitivity, which illustrates a high potential f…

0301 basic medicinelcsh:QH426-470Taxonomic classificationADNCodificació Teoria de laBiologyBioinformaticsMachine learningcomputer.software_genreDNA; genes; taxonomic classification; convolutional neural networks; encodingConvolutional neural networkArticle03 medical and health sciences0302 clinical medicineBiologia -- ClassificacióEncoding (memory)convolutional neural networksGeneticstaxonomic classificationSensitivity (control systems)genesGenetics (clinical)ta113Biology -- Classificationbusiness.industryBiological classificationCoding theoryDNAencodinglcsh:Genetics030104 developmental biologyGenes030220 oncology & carcinogenesisEncodingConvolutional neural networksArtificial intelligenceCoding theorybusinesscomputerGens
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Music style not only modulates the auditory cortex, but also motor related areas

2021

The neuroscience of music has recently attracted significant attention, but the effect of music style on the activation of auditory-motor regions has not been explored. The aim of the present study is to analyze the differences in brain activity during passive listening to non-vocal excerpts of four different music genres (classical, reggaeton, electronic and folk). A functional magnetic resonance imaging (fMRI) experiment was performed. Twenty-eight participants with no musical training were included in the study. They had to passively listen to music excerpts of the above genres during fMRI acquisition. Imaging analysis was performed at the whole-brain-level and in auditory-motor regions …

0301 basic medicinemedicine.medical_specialtyBrain activity and meditationmusiikkigenretmusiikkipsykologiaMusicalAudiologyAuditory cortexbehavioral disciplines and activitiesStyle (sociolinguistics)03 medical and health sciences0302 clinical medicinetoiminnallinen magneettikuvausmedicineHumansActive listeningmusicmotoriikkaAuditory Cortexmusic genreBrain Mappingmedicine.diagnostic_testGeneral NeurosciencefMRIMotor CortexMagnetic Resonance ImaginghumanitiesImaging analysisClassical music030104 developmental biologyaivokuoriAcoustic Stimulationauditory-motor areasAuditory PerceptionPsychologyFunctional magnetic resonance imaginghuman activities030217 neurology & neurosurgeryMusicpsychological phenomena and processesärsykkeet
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WES/WGS Reporting of Mutations from Cardiovascular "Actionable" Genes in Clinical Practice: A Key Role for UMD Knowledgebases in the Era of Big Datab…

2016

International audience; High-throughput next-generation sequencing such as whole-exome and whole-genome sequencing are being rapidly integrated into clinical practice. The use of these techniques leads to the identification of secondary variants for which decisions about the reporting or not to the patient need to be made. The American College of Medical Genetics and Genomics recently published recommendations for the reporting of these variants in clinical practice for 56 "actionable" genes. Among these, seven are involved in Marfan Syndrome And Related Disorders (MSARD) resulting from mutations of the FBN1, TGFBR1 and 2, ACTA2, SMAD3, MYH11 and MYLK genes. Here, we show that mutations col…

0301 basic medicinemedicine.medical_specialtyKnowledge BasesGenomicsmarfan-syndrome[SDV.GEN.GH] Life Sciences [q-bio]/Genetics/Human genetics030105 genetics & heredityBiologycomputer.software_genreGenomeExAC03 medical and health sciencesAnnotationincidental findingsGeneticsmedicineHumanspathogenicityGenetic Predisposition to Diseasetgfbr2ExomegenomeESPGenetics (clinical)Exome sequencing[INFO.INFO-BI] Computer Science [cs]/Bioinformatics [q-bio.QM]variantsDatabasethoracic aortic-aneurysmsGenome HumanHigh-Throughput Nucleotide SequencingMYLKGenomicspredictionmutations3. Good healthMarfan syndrome030104 developmental biologydissection[SDV.GEN.GH]Life Sciences [q-bio]/Genetics/Human geneticsCardiovascular DiseasesMutationMedical geneticsIdentification (biology)LSDB[INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM]computerexome
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Editorial: Protein Interaction Networks in Health and Disease

2016

The identification and annotation of protein-protein interactions (PPIs) is of great importance in systems biology. Big data produced from experimental or computational approaches allow not only the construction of large protein interaction maps but also expand our knowledge on how proteins build up molecular complexes to perform sophisticated tasks inside a cell. However, if we want to accurately understand the functionality of these complexes, we need to go beyond the simple identification of PPIs. We need to know when and where an interaction happens in the cell and also understand the flow of information through a protein interaction network. Another perspective of the research on PPI n…

0301 basic medicineprotein networkdiseasePhysiologySystems biologyCellular homeostasissystems biologyComputational biologyprotein functionBiologyProteomicscomputer.software_genreprotein interactionsInteractomeProtein–protein interaction03 medical and health sciences030104 developmental biologyHuman interactomeInteraction networkGeneticsMolecular MedicineData miningcomputerGenetics (clinical)Biological networkFrontiers in Genetics
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Arm Hypervisor and Trustzone Alternatives

2020

Many scenarios such as DRM, payments, and homeland security require a trusted and verified trusted execution environment (TEE) on ARM. In most cases such TEE should be available in source code mode. The vendor cannot conduct code review and ensure that the operating system is trustworthy unless source code is available. Android and other rich execution environments (REEs) support various TEE implementations. Each TEE implementation has its own unique way of deploying trusted applications and features. Most TEEs in ARM can be started at TrustZone™ or Hyp (Hypervisor) mode. Choosing a proper TEE operating system can be a problem for trusted application developers and hardware vendors. This ar…

0303 health sciences03 medical and health sciencesComputer science0202 electrical engineering electronic engineering information engineeringOperating system020206 networking & telecommunicationsHypervisor02 engineering and technologycomputer.software_genrecomputer030304 developmental biology
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Grapes: a method and a SAS program for graphical representations of assessor performances

1994

GRAPES computes individual and global analyses of variance for sensory profiling data, consisting of several sessions in which all the panelists gave scores to all the products for a number of attributes. The fitted model takes into account the session effect. GRAPES summarizes the results by means of graphical assessor scatterplots which allow to check and to compare panelist performances, such as the way of using scale, the reliability, the discrimination power and the agreement with the panel. In addition, GRAPES detects the outliers for each of these criterion. The usefulness of GRAPES for the panel leader will be demonstrated using texture and flavor profiling of 4 restructured steaks …

0303 health sciences030309 nutrition & dieteticsComputer sciencebusiness.industry[SDV]Life Sciences [q-bio]Computer aidTEST DE CONSOMMATION04 agricultural and veterinary sciencescomputer.software_genre040401 food scienceSensory analysisSensory Systems[SDV] Life Sciences [q-bio]03 medical and health sciences0404 agricultural biotechnologyOutlierProfiling (information science)Data miningArtificial intelligenceGraphicsbusinesscomputerComputingMilieux_MISCELLANEOUSFood Science
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Boosting Signal-to-Noise in Complex Biology: Prior Knowledge Is Power

2011

A major difficulty in the analysis of complex biological systems is dealing with the low signal-to-noise inherent to nearly all large biological datasets. We discuss powerful bioinformatic concepts for boosting signal-to-noise through external knowledge incorporated in processing units we call filters and integrators. These concepts are illustrated in four landmark studies that have provided model implementations of filters, integrators, or both.

0303 health sciencesLandmarkBoosting (machine learning)Biochemistry Genetics and Molecular Biology(all)business.industryBiologyMachine learningcomputer.software_genreBioinformaticsGeneral Biochemistry Genetics and Molecular Biology03 medical and health sciences0302 clinical medicine030220 oncology & carcinogenesisIntegratorArtificial intelligencebusinesscomputerImplementation030304 developmental biologyCell
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