Offers a platform dealing with gene expression data on haematopoietic cell types. Haemosphere gathers manually curated datasets including Haemopedia, a library of microarray gene expression profiles from wildtype murine blood cells. It allows users to: (1) search genes of interest, (2) browse expression profiles, (3) run differential expression analyses and (4) handle sets of gene. This platform assists researchers to investigate genes involved in an aspect of hematopoiesis or to discover pathways.
Supplies information about microarrays. BB is a USDA (United States Department of Agriculture)-funded public database for cereal microarray data that includes microarray experiment annotations. It can discover all the known facts about any probe set or exemplar sequence on the chip and then compare them with other plant species. Each data query is analyzed and a visualization tool allows users to explore their data.
Allows users to access gene expression profiles. CellMontage permits users to access expression data in a content-based manner. Query profiles are searched against a database to find profiles with similar overall gene expression. This database includes a search engine using a customized algorithm for the efficient calculation of the correlation coefficient between profiles measuring different sets of genes.
Contains data related to Alzheimer's disease and consists of genomics, proteomics, metabolomics and other data types from a variety of human studies, animal and cellular model systems. AMPAD Knowledge Portal generates data which have been inspected with the NIA programs: Accelerating Medicines Partnership-Alzheimer’s Disease - Target Discovery and Preclinical Validation Project AMPAD and Molecular Mechanisms of the Vascular Etiology of Alzheimer’s Disease M²OVEAD Consortium.
Provides several data about gene expression microarrays across diseases. MetaSignature allows users to identify consistent gene expression signatures in a specific disease state. It enables researchers to pursue data-driven hypotheses, and permits to explore gene expression meta-analysis data from either diseases or genes of interest. The research is focused on the strongest molecular evidence in order to researcher can break the self-perpetuating annotation inequality cycle that results in research bias.
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