Search results for "Brain atlas"

showing 4 items of 4 documents

Analysis of low-correlated spatial gene expression patterns: A clustering approach in the mouse brain data hosted in the Allen Brain Atlas

2018

The Allen Brain Atlas (ABA) provides a similar gene expression dataset by genome-scale mapping of the C57BL/6J mouse brain. In this study, the authors describe a method to extract the spatial information of gene expression patterns across a set of 1047 genes. The genes were chosen from among the 4104 genes having the lowest Pearson correlation coefficient used to compare the expression patterns across voxels in a single hemisphere for available coronal and sagittal volumes. The set of genes analysed in this study is the one discarded in the article by Bohland et al. , which was considered to be of a lower consistency, not a reliable dataset. Following a normalisation task with a global and …

0301 basic medicineImage registrationGenomicsBiologycomputer.software_genre03 medical and health sciencessymbols.namesake0302 clinical medicineVoxelmedicineCluster analysisSpatial analysisSettore INF/01 - Informaticabusiness.industryBrain atlasPattern recognitionSagittal planePearson product-moment correlation coefficient030104 developmental biologymedicine.anatomical_structuresymbolsMorphometric similarity cluster analysis gene expression patternsComputer Vision and Pattern RecognitionArtificial intelligencebusinesscomputer030217 neurology & neurosurgerySoftware
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Clustering of low-correlated spatial gene expression patterns in the mouse brain in the Allen Brain Atlas

2018

In this paper, clustering techniques are applied to spatial gene expression patterns with a low genomic correlation between the sagittal and coronal projections. The data analysed here are hosted on an available public DB named ABA (Allen Brain Atlas). The results are compared to those obtained by Bohland et al. on the complementary dataset (high correlation values). We prove that, by analysing a reduced dataset,hence reducing the computational burden, we get the same accuracy in highlighting different neuroanatomical region.

0301 basic medicineSettore INF/01 - InformaticaComputer scienceBrain atlasComputer Science ApplicationGenomicsComputational biologySagittal planeCorrelation03 medical and health sciences030104 developmental biology0302 clinical medicinemedicine.anatomical_structureComputer Networks and CommunicationHardware and ArchitectureCoronal planeGene expressionmedicineComputer Vision and Pattern RecognitionElectrical and Electronic EngineeringCluster analysis030217 neurology & neurosurgery
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2007

Cellular and Molecular Neurosciencemedicine.anatomical_structuremedia_common.quotation_subjectCentral nervous systemBrain atlasmedicineArtNeurosciencemedia_commonJournal of Chemical Neuroanatomy
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2015

We present a method to discover discriminative brain metabolism patterns in [18F] fluorodeoxyglucose positron emission tomography (PET) scans, facilitating the clinical diagnosis of Alzheimer's disease. In the work, the term "pattern" stands for a certain brain region that characterizes a target group of patients and can be used for a classification as well as interpretation purposes. Thus, it can be understood as a so-called "region of interest (ROI)". In the literature, an ROI is often found by a given brain atlas that defines a number of brain regions, which corresponds to an anatomical approach. The present work introduces a semi-data-driven approach that is based on learning the charac…

Multidisciplinarymedicine.diagnostic_testbusiness.industryComputer scienceModel selectionBrain atlasMagnetic resonance imagingPattern recognitionMixture modelmedicine.diseasecomputer.software_genreBrain regionNeuroimagingDiscriminative modelPositron emission tomographyVoxelRegion of interestmedicineArtificial intelligenceAlzheimer's diseaseNuclear medicinebusinesscomputerAlzheimer's Disease Neuroimaging InitiativePLOS ONE
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