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3,272 results for “microarray”
Comparative Microarray Analysis in Primary Cutaneous Malignant Melanoma
ClinicalTrials.gov study NCT01482260. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Diagnostic and Therapeutic Applications of Microarrays in Lung Transplantation
ClinicalTrials.gov study NCT02812290. IPD Sharing: UNDECIDED. Countries: 6. Publications: 9.
Diagnostic and Therapeutic Applications of Microarrays in Liver Transplantation
ClinicalTrials.gov study NCT03193151. IPD Sharing: NO. Countries: 5. Publications: 5.
Multiplex PCR -Microarray-based Assay in the Detection of Mycobacterium
ClinicalTrials.gov study NCT03794284. IPD Sharing: NO. Countries: 1. Publications: 11.
Childhood Cancer and Plexiform Neurofibroma Tissue Microarray for Molecular Target Screening and Clinical Drug Development
ClinicalTrials.gov study NCT00340522. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Prenatal Microarray Follow-Up Study
ClinicalTrials.gov study NCT02160938. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Multiplex Microarray Chip-Based Diagnosis of Respiratory Infections
ClinicalTrials.gov study NCT01212042. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Microarray Analysis in Syndromic Obesity
ClinicalTrials.gov study NCT01043198. IPD Sharing: Not stated. Countries: 1. Publications: 8.
Data from: Genomics of Compositae weeds: EST libraries, microarrays, and evidence of introgression
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Data from: Transcriptome sequencing and microarray development for the woodrat (Neotoma spp.): custom genetic tools for exploring herbivore ecology
Massively parallel sequencing has enabled the creation of novel, in-depth genetic tools for nonmodel, ecologically important organisms. We present the de novo transcriptome sequencing, analysis and microarray development for a vertebrate herbivore, the woodrat (Neotoma spp.). This genus is of ecological and evolutionary interest, especially with respect to ingestion and hepatic metabolism of potentially toxic plant secondary compounds. We generated a liver transcriptome of the desert woodrat (Neotoma lepida) using the Roche 454 platform. The assembled contigs were well annotated using rodent references (99.7% annotation), and biotransformation function was reflected in the gene ontology. The transcriptome was used to develop a custom microarray (eArray, Agilent). We tested the microarray with three experiments: one across species with similar habitat (thus, dietary) niches, one across species with different habitat niches and one across populations within a species. The resulting one-colour arrays had high technical and biological quality. Probes designed from the woodrat transcriptome performed significantly better than functionally similar probes from the Norway rat (Rattus norvegicus). There were a multitude of expression differences across the woodrat treatments, many of which related to biotransformation processes and activities. The pattern and function of the differences indicate shared ecological pressures, and not merely phylogenetic distance, play an important role in shaping gene expression profiles of woodrat species and populations. The quality and functionality of the woodrat transcriptome and custom microarray suggest these tools will be valuable for expanding the scope of herbivore biology, as well as the exploration of conceptual topics in ecology.
Data from: A revised design for microarray experiments to account for experimental noise and uncertainty of probe response
Background: Although microarrays are analysis tools in biomedical research, they are known to yield noisy output that usually requires experimental confirmation. To tackle this problem, many studies have developed rules for optimizing probe design and devised complex statistical tools to analyze the output. However, less emphasis has been placed on systematically identifying the noise component as part of the experimental procedure. One source of noise is the variance in probe binding, which can be assessed by replicating array probes. The second source is poor probe performance, which can be assessed by calibrating the array based on a dilution series of target molecules. Using model experiments for copy number variation and gene expression measurements, we investigate here a revised design for microarray experiments that addresses both of these sources of variance. Results: Two custom arrays were used to evaluate the revised design: one based on 25 mer probes from an Affymetrix design and the other based on 60 mer probes from an Agilent design. To assess experimental variance in probe binding, all probes were replicated ten times. To assess probe performance, the probes were calibrated using a dilution series of target molecules and the signal response was fitted to an adsorption model. We found that significant variance of the signal could be controlled by averaging across probes and removing probes that are nonresponsive or poorly responsive in the calibration experiment. Taking this into account, one can obtain a more reliable signal with the added option of obtaining absolute rather than relative measurements. Conclusion: The assessment of technical variance within the experiments, combined with the calibration of probes allows to remove poorly responding probes and yields more reliable signals for the remaining ones. Once an array is properly calibrated, absolute quantification of signals becomes straight forward, alleviating the need for normalization and reference hybridizations.
External microarray datasets for manuscript entitled "Downregulated dual-specificity protein phosphatase 1 in ovarian carcinoma: a comprehensive study with multiple methods"
<p>Microarrays related to ovarian carcinoma from GEO database</p>
Raw Image Data Repository: Repurposing Large-Format Microarrays for Scalable Spatial Transcriptomics
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Illumina microarray data for our protocol
<p>Illumina microarray data for our protocol</p>
Microarray Analysis for Human Genetic Disease
ClinicalTrials.gov study NCT00001898. IPD Sharing: Not stated. Countries: 4. Publications: 1.
Gene Expression Profiles in Generalized Aggressive Periodontitis: A Gene Network-based Microarray Analysis
ClinicalTrials.gov study NCT02327533. IPD Sharing: Not stated. Countries: 0. Publications: 3.
Centocor Microarray Study of Patients
ClinicalTrials.gov study NCT00462072. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Trial for Microarray Analysis of Colon Cancer Outcome-A (MACCO-A)
ClinicalTrials.gov study NCT00127036. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Data from: Transcriptome sequencing and microarray development for the woodrat (Neotoma spp.): custom genetic tools for exploring herbivore ecology
Open the record for dataset details and reuse information.
Data from: A revised design for microarray experiments to account for experimental noise and uncertainty of probe response
Open the record for dataset details and reuse information.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.