Search results for "INFORMATICS"

showing 10 items of 2542 documents

GenClust: A genetic algorithm for clustering gene expression data

2005

Abstract Background Clustering is a key step in the analysis of gene expression data, and in fact, many classical clustering algorithms are used, or more innovative ones have been designed and validated for the task. Despite the widespread use of artificial intelligence techniques in bioinformatics and, more generally, data analysis, there are very few clustering algorithms based on the genetic paradigm, yet that paradigm has great potential in finding good heuristic solutions to a difficult optimization problem such as clustering. Results GenClust is a new genetic algorithm for clustering gene expression data. It has two key features: (a) a novel coding of the search space that is simple, …

Clustering high-dimensional dataDNA ComplementaryComputer scienceRand indexCorrelation clusteringOligonucleotidesEvolutionary algorithmlcsh:Computer applications to medicine. Medical informaticscomputer.software_genreBiochemistryPattern Recognition AutomatedBiclusteringOpen Reading FramesStructural BiologyCURE data clustering algorithmConsensus clusteringGenetic algorithmCluster AnalysisCluster analysislcsh:QH301-705.5Molecular BiologyGene expression data Clustering Evolutionary algorithmsOligonucleotide Array Sequence AnalysisModels StatisticalBrown clusteringHeuristicGene Expression ProfilingApplied MathematicsComputational BiologyComputer Science Applicationslcsh:Biology (General)Gene Expression RegulationMutationlcsh:R858-859.7Data miningSequence AlignmentcomputerSoftwareAlgorithmsBMC Bioinformatics
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Bayesian versus data driven model selection for microarray data

2014

Clustering is one of the most well known activities in scientific investigation and the object of research in many disciplines, ranging from Statistics to Computer Science. In this beautiful area, one of the most difficult challenges is a particular instance of the model selection problem, i.e., the identification of the correct number of clusters in a dataset. In what follows, for ease of reference, we refer to that instance still as model selection. It is an important part of any statistical analysis. The techniques used for solving it are mainly either Bayesian or data-driven, and are both based on internal knowledge. That is, they use information obtained by processing the input data. A…

Clustering Model selection Bayesian information criterion Akaike information criterion Minimum message length BioinformaticsSettore INF/01 - InformaticaComputer sciencebusiness.industryModel selectionBayesian probabilitycomputer.software_genreMachine learningComputer Science ApplicationsData-drivenDetermining the number of clusters in a data setIdentification (information)Bayesian information criterionData miningArtificial intelligenceAkaike information criterionCluster analysisbusinesscomputer
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Modality-specific dysfunctional neural processing of social-abstract and non-social-concrete information in schizophrenia

2021

Highlights • Social/non-social information processing in three modalities was investigated in SZ. • SZ showed reduced activation for social information only in gesture modality. • Reduced activation in SZ was observed for non-social information only in speech. • Neural Neural processing in bimodal condition is not different between patients and controls.

Cognitive NeuroscienceSchizoaffective disorderDysfunctional familylcsh:Computer applications to medicine. Medical informaticsmPFC050105 experimental psychologylcsh:RC346-42903 medical and health sciencesGesture0302 clinical medicineSocialmedicineImage Processing Computer-AssistedHumansSpeech0501 psychology and cognitive sciencesRadiology Nuclear Medicine and imaging10. No inequalityPrefrontal cortexlcsh:Neurology. Diseases of the nervous systemBrain MappingModality (human–computer interaction)medicine.diagnostic_testGestures05 social sciencesRegular ArticleMultimodal processingmedicine.diseaseMagnetic Resonance ImagingNeurologySchizophreniaNeural processingSchizophrenialcsh:R858-859.7Neurology (clinical)PsychologyFunctional magnetic resonance imaging030217 neurology & neurosurgeryCognitive psychologyGestureNeuroImage: Clinical
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FlyMove – a new way to look at development of Drosophila

2003

Development of any organism requires a complex interplay of genes to orchestrate the many movements needed to build up an embryo. Previously, work on Drosophila melanogaster has provided important insights that are often applicable in other systems. But developmental processes, which take place in space and time, are difficult to convey in textbooks. Here, we introduce FlyMove (http://flymove.uni-muenster.de), a new database combining movies, animated schemata, interactive "modules" and pictures that will greatly facilitate the understanding of Drosophila development.

Cognitive scienceanimal structuresDatabases FactualbiologyComputational BiologyGenes Insectbiology.organism_classificationBioinformaticsDrosophila melanogasterComputingMethodologies_PATTERNRECOGNITIONDevelopment (topology)Gene Expression RegulationMorphogenesisGeneticsAnimalsComputer SimulationFemaleDrosophila melanogasterDrosophilaOrganismTrends in Genetics
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Factors in the global assimilation of collaborative information technologies: an exploratory investigation in five regions

2008

The diffusion of innovation theory is deployed to investigate the global assimilation of collaborative information technologies (CITs). Based on the concepts of IT acquisition and utilization, an assimilation framework is presented to highlight four states (limited, focused, lagging, and pervasive) that capture the assimilation of conferencing and groupware CITs. Data collected from 538 organizations in the United States, Australia, Hong Kong, Norway, and Switzerland are aggregated and analyzed to explore assimilation patterns and the influence of decision-making pattern, functional integration, promotion of collaboration, organization size, and IT function size on the assimilation of CITs.…

Collaborative softwareInformation Systems and ManagementKnowledge managementDiffusion of innovation theory10009 Department of Informaticsbusiness.industrymedia_common.quotation_subjectInformation technologyAssimilation (biology)1803 Management Science and Operations Research000 Computer science knowledge & systemsManagement Science and Operations ResearchComputer Science ApplicationsManagement Information Systems1404 Management Information SystemsPromotion (rank)Geography1706 Computer Science Applications1802 Information Systems and ManagementbusinessLaggingFunction (engineering)media_common
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Genome-Wide Association Study of Diabetic Kidney Disease Highlights Biology Involved in Glomerular Basement Membrane Collagen

2019

BACKGROUND: Although diabetic kidney disease demonstrates both familial clustering and single nucleotide polymorphism heritability, the specific genetic factors influencing risk remain largely unknown.METHODS: To identify genetic variants predisposing to diabetic kidney disease, we performed genome-wide association study (GWAS) analyses. Through collaboration with the Diabetes Nephropathy Collaborative Research Initiative, we assembled a large collection of type 1 diabetes cohorts with harmonized diabetic kidney disease phenotypes. We used a spectrum of ten diabetic kidney disease definitions based on albuminuria and renal function.RESULTS: Our GWAS meta-analysis included association result…

Collagen Type IVMale0301 basic medicineEXPRESSIONNEPHROPATHY030232 urology & nephrologyPROTEINGenome-wide association studyRECEPTOR TYROSINE KINASESBiologySUSCEPTIBILITYBioinformaticsurologic and male genital diseasesAutoantigensNephropathyEnd stage renal diseaseCohort StudiesDiabetic nephropathy03 medical and health sciences0302 clinical medicineSDG 3 - Good Health and Well-beingDiabetes mellitusGlomerular Basement MembranemedicineHumansDiabetic NephropathiesAlport syndromeLetter to the EditorCOMPLICATIONSNITRIC-OXIDEMUTATIONS1184 Genetics developmental biology physiologyGeneral Medicinemedicine.diseaseGENE3. Good healthDiabetes Mellitus Type 1030104 developmental biologyNephrology3121 General medicine internal medicine and other clinical medicineMutationAlbuminuria/dk/atira/pure/sustainabledevelopmentgoals/good_health_and_well_beingFemale3111 Biomedicinemedicine.symptomCOLLECTIN 11 CL-11Genome-Wide Association StudyKidney disease
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Modelling swarm-intelligent systems for medical applications

2017

Modeling swarm intelligent systems has attracted attention of researchers over the last decade, as the attributes such as self-organization, self-regulation or collective behavior exhibited by the system entities while following a certain set of rules, can be implemented with the aim at investigating complexity of the problems that an individual would be unable to tackle in real world. In this keynote paper, meta-heuristics and paradigms of modeling swarm-intelligent systems will be discussed with respect to their application areas for medical purposes.

Collective behaviorswarm intelligenceComputer sciencebusiness.industryIntelligent decision support systemCollective intelligenceSwarm behaviourcollective intelligencebioinformaticsSwarm intelligenceSet (abstract data type)modeling medical systemsApplication areasArtificial intelligencebusiness2017 Twelfth International Conference on Digital Information Management (ICDIM)
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An inducible mouse model of colon carcinogenesis for the analysis of sporadic and inflammation-driven tumor progression.

2007

Colorectal cancer is a life-threatening disease that can develop spontaneously or as a complication of inflammatory bowel diseases. Mouse models are essential tools for the preclinical testing of novel therapeutic options in vivo. Here, we provide a highly reliable protocol for an experimental mouse model to study the development of colon cancers. It is based on the mutagenic agent azoxymethane (AOM), which exerts colonotropic carcinogenicity. Repeated intraperitoneal administration of AOM results in the development of spontaneous tumors within 30 weeks. As an alternative option, inflammation-dependent tumor growth can be investigated by combining the administration of AOM with the inflamma…

Colorectal cancerAzoxymethaneInflammationDiseaseTumor initiationBiologyBioinformaticsGeneral Biochemistry Genetics and Molecular Biologychemistry.chemical_compoundMiceIn vivomedicineAnimalsCarcinogenAzoxymethaneDextran Sulfatemedicine.diseaseDisease Models AnimalchemistryTumor progressionColonic NeoplasmsCancer researchCarcinogensDisease Progressionmedicine.symptomInflammation MediatorsMutagensNature protocols
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Prognostic and predictive factors in colorectal cancer: Kirsten Ras in CRC (RASCAL) and TP53CRC collaborative studies.

2005

Mutations in the Ki-ras and TP53 genes are the most frequently observed genetic alterations in colorectal cancer (CRC). Ki-ras mutations are mostly found in codons 12 and 13, and less in codon 61. The majority of the TP53 mutations occur in the core domain which contains the sequence-specific DNA binding activity of the protein, and they results in loss of DNA binding. Few centres have sufficient patients to collect detailed information in the large numbers required to determine the impact of individual ki-ras and TP53 genotypes on outcome. Moreover, it has been reported that specific genetic alterations, and not any mutation, might play a different biological role in cancer progression. Fo…

Colorectal cancerBiologymedicine.disease_causeBioinformaticsProto-Oncogene Proteins p21(ras)Predictive Value of TestsProto-Oncogene ProteinsGenotypemedicineneoplasmsSurvival rateMutationCancerHematologyPrognosismedicine.diseasePrimary tumorProto-Oncogene Proteins p21(ras)Survival RateOncologyMeta-analysisMutationras ProteinsCancer researchFluorouracilTumor Suppressor Protein p53Colorectal Neoplasms
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Snapshot liver transcriptome in hepatocellular carcinoma

2012

Lately, advances in high throughput technologies in biomedical research have led to a dramatic increase in the accessibility of molecular insights at different levels of cancer biology such as genome, epigenome, transcriptome, proteome, and others. Among the diverse biological layers, the transcriptome has been most extensively studied especially due to the successful and broad introduction of the microarray technology. The future prospect of broad disposability of deep sequencing technology will furthermore lead to a more sensitive detection of lowly expressed transcripts and to an increase in the number of newly identified transcripts, but also to increase the discovery and characterizati…

Comparative genomicsGeneticsCarcinoma HepatocellularHepatologyHepatocellular carcinomaBioinformaticsComparative genomicsAlternative splicingLiver NeoplasmsEpigenomeBiologyGenomeDeep sequencingTranscriptomeGene Expression Regulation NeoplasticLiverComparative transcriptomicsProteomeGene chip analysisGeneticsHumansHCCTranscriptomeJournal of Hepatology
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