Search results for " PREDICTION"

showing 10 items of 366 documents

Extracting similar sub-graphs across PPI Networks

2009

Singling out conserved modules (corresponding to connected sub-graphs) throughout protein-protein interaction networks of different organisms is a main issue in bioinformatics because of its potential applications in biology. This paper presents a method to discover highly matching sub-graphs in such networks. Sub-graph extraction is carried out by taking into account, on the one side, both protein sequence and network structure similarities and, on the other side, both quantitative and reliability information possibly available about interactions. The method is conceived as a generalization of a known technique, able to discover functional orthologs in interaction networks. Some preliminar…

Protein structure databaseBioinformatics network analysisProtein sequencingMatching (graph theory)GeneralizationComputer scienceReliability (computer networking)Protein function predictionGraph theoryData miningcomputer.software_genrecomputerNetwork analysis
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A computer system to perform structure comparison using TOPS representations of protein structure

2001

We describe the design and implementation of a fast topology-based method for protein structure comparison. The approach uses the TOPS topological representation of protein structure, aligning two structures using a common discovered pattern and generating measure of distance derived from an insert score. Heavy use is made of a constraint-based pattern-matching algorithm for TOPS diagrams that we have designed and described elsewhere (Bioinformatics 15(4) (1999) 317). The comparison system is maintained at the European Bioinformatics Institute and is available over the Web at tops.ebi.ac.uk/tops. Users submit a structure description in Protein Data Bank (PDB) format and can compare it with …

Protein structure databaseMeasure (data warehouse)Molecular StructureComputer scienceGeneral Chemical EngineeringProteinsSequence Homologycomputer.file_formatTOPSProtein structure predictioncomputer.software_genreProtein Data BankApplied Microbiology and BiotechnologyPattern Recognition AutomatedArtificial IntelligencePattern matchingData miningProtein topologyRepresentation (mathematics)computerAlgorithmsSoftwareBiotechnology
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Experimental Evaluation of Protein Secondary Structure Predictors

2009

Understanding protein biological function is a key issue in modern biology, which is largely determined by its 3D shape. Protein 3D shape, in its turn, is functionally implied by its amino acid sequence. Since the direct inspection of such 3D structures is rather expensive and time consuming, a number of software techniques have been developed in the last few years that predict a spatial model, either of the secondary or of the tertiary form, for a given target protein starting from its amino acid sequence. This paper offers a comparison of several available automatic secondary structure prediction tools. The comparison is of the experimental kind, where two relevant sets of proteins, a non…

Protein structure databasebusiness.industryProtein structure predictionBioinformaticsMachine learningcomputer.software_genreSet (abstract data type)Bioinformatics Protein PredictionTest caseGlobal distance testArtificial intelligenceCASPbusinessPeptide sequencecomputerProtein secondary structure
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Protein Structure Metapredictors

2013

Protein structures prediction
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Using Deep Learning to Extrapolate Protein Expression Measurements

2020

Mass spectrometry (MS)-based quantitative proteomics experiments typically assay a subset of up to 60% of the ≈20 000 human protein coding genes. Computational methods for imputing the missing values using RNA expression data usually allow only for imputations of proteins measured in at least some of the samples. In silico methods for comprehensively estimating abundances across all proteins are still missing. Here, a novel method is proposed using deep learning to extrapolate the observed protein expression values in label-free MS experiments to all proteins, leveraging gene functional annotations and RNA measurements as key predictive attributes. This method is tested on four datasets, in…

ProteomicsIn silicoQuantitative proteomicsComputational biologyBiologyBiochemistryprotein abundance predictionMass SpectrometryProtein expressionMice03 medical and health sciencesDeep LearningAbundance (ecology)AnimalsMolecular BiologyGeneResearch Articles030304 developmental biologydeep learning networks0303 health sciencesUniProt keywordsbusiness.industryDeep learning030302 biochemistry & molecular biologyProteinsRNAMolecular Sequence AnnotationMissing dataGene OntologyArtificial intelligencebusinessResearch ArticlePROTEOMICS
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Prognostic Value of Troponins in Patients With or Without Coronary Heart Disease: Is it Dependent on Structure and Biology?

2020

Convincing evidence has emerged that cardiac troponins (cTns) T and I are the biochemical gold standard for diagnosing cardiac injury, and may also be used as efficient screening and risk stratification tools, especially when measured with the new high-sensitivity (hs-) immunoassays. In this narrative review, we aim to explore and critically discuss the results of recent epidemiological studies that have attempted to characterise the prognostic value of cTns in patients with or without cardiovascular disease, and then interpret this information according to cTn biology. Overall, all recent studies agree that higher blood levels of cTns reflect the larger risk of cardiovascular events and/or…

Pulmonary and Respiratory Medicinemedicine.medical_specialtyCoronary heart disease; Mortality; Prediction; Risk stratification; TroponinPopulationCoronary DiseaseDisease030204 cardiovascular system & hematologyBioinformatics03 medical and health sciences0302 clinical medicineTroponin complexTroponin TEpidemiologyTroponin ImedicineHumans030212 general & internal medicineMortalityeducationRisk stratificationeducation.field_of_studybiologybusiness.industryC-reactive proteinTroponin IGold standard (test)TroponinTroponinCoronary heart diseaseC-Reactive Proteinbiology.proteinCardiology and Cardiovascular MedicinebusinessPredictionBiomarkers
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New set of 2D/3D thermodynamic indices for proteins. A formalism based on "Molten Globule" theory

2010

Abstract We define eight new macromolecular indices, and several related descriptors for proteins. The coarse grained methodology used for its deduction ensures its fast execution and becomes a powerful potential tool to explore large databases of protein structures. The indices are intended for stability studies, predicting Φ -values, predicting folding rate constants, protein QSAR/QSPR as well as protein alignment studies. Also, these indices could be used as scoring function in protein-protein docking or 3D protein structure prediction algorithms and any others applications which need a numerical code for proteins and/or residues from 2D or 3D format.

Quantitative structure–activity relationshipComputer sciencePhysics and Astronomy(all)Protein structure predictionMolten globuleFolding degreeFormalism (philosophy of mathematics)Protein indicesProtein structureFPIDocking (molecular)Protein stabilityPhysical chemistryBiological systemStatistical potentialMacromoleculeProtein folding descriptor
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Search for the Associated Production of the Standard-Model Higgs Boson in the All-Hadronic Channel

2009

We report on a search for the standard-model Higgs boson in pp collisions at s=1.96 TeV using an integrated luminosity of 2.0 fb(-1). We look for production of the Higgs boson decaying to a pair of bottom quarks in association with a vector boson V (W or Z) decaying to quarks, resulting in a four-jet final state. Two of the jets are required to have secondary vertices consistent with B-hadron decays. We set the first 95% confidence level upper limit on the VH production cross section with V(-> qq/qq('))H(-> bb) decay for Higgs boson masses of 100-150 GeV/c(2) using data from run II at the Fermilab Tevatron. For m(H)=120 GeV/c(2), we exclude cross sections larger than 38 times the standard-m…

QuarkParticle physicsStandardsFinal stateFermilab TevatronHiggs bosonTevatronFOS: Physical sciencesGeneral Physics and AstronomyElementary particleddc:500.201 natural sciences114 Physical sciencesStandard ModelVector bosonHigh Energy Physics - ExperimentNuclear physicsHigh Energy Physics - Experiment (hep-ex)Particle decayTellurium compounds0103 physical sciencesJetsB-hadron decaysHigh energy physics010306 general physicsBosonsBosonStandard-model Higgs bosonsPhysicsIntegrated luminosityHIGGS BOSONModel predictionCross section010308 nuclear & particles physicsPhysicsHigh Energy Physics::PhenomenologyConfidence levelsUpper limits3. Good healthVector bosonProduction cross sectionBottom quarksSecondary verticesHiggs bosonCDFHigh Energy Physics::Experiment
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THERP and HEART integrated methodology for human error assessment

2015

Abstract THERP and HEART integrated methodology is proposed to investigate accident scenarios that involve operator errors during high-dose-rate (HDR) treatments. The new approach has been modified on the basis of fuzzy set concept with the aim of prioritizing an exhaustive list of erroneous tasks that can lead to patient radiological overexposures. The results allow for the identification of human errors that are necessary to achieve a better understanding of health hazards in the radiotherapy treatment process, so that it can be properly monitored and appropriately managed.

RadiationComputer scienceProcess (engineering)Medical cyclotronHuman errorFuzzy setTechnique for Human Error Rate PredictionFuzzy logicReliability engineeringIdentification (information)PETRadiotherapy treatmentRadiopharmaceuticalsRadioactive air effluentSettore ING-IND/19 - Impianti Nucleari
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The upgraded ISOLDE yield database – A new tool to predict beam intensities

2020

At the CERN-ISOLDE facility a variety of radioactive ion beams are available to users of the facility. The number of extractable isotopes estimated from yield database data exceeds 1000 and is still increasing. Due to high demand and scarcity of available beam time, precise experiment planning is required. The yield database stores information about radioactive beam yields and the combination of target material and ion source needed to extract a certain beam along with their respective operating conditions. It allows to investigate the feasibility of an experiment and the estimation of required beamtime. With the increasing demand for ever more exotic beams, needs arise to extend the functi…

Radioactive ion beamsNuclear and High Energy PhysicsYieldsComputer sciencecomputer.software_genre114 Physical sciences01 natural sciencesISOLDEDatabaseFLUKACERN0103 physical sciencesddc:530Production Yield010306 general physicsInstrumentationLarge Hadron ColliderDatabase010308 nuclear & particles physicsIn-target productionYield predictionCross sectionsYield (chemistry)ABRABLAIONIZATIONRelease efficiencycomputerRadioactive beamBeam (structure)Radioactive beamsNuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms
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