GeneAnalytics enables researchers to identify tissues and cell types related to their gene sets, to characterize tissue samples and cultured cells and assess their purity and explore their selective markers.
The key strength of GeneAnalytics stems from the extensive manually curated gene expression data available in LifeMap Discovery.Learn more >
GeneAnalytics enables researchers to identify diseases related to their gene sets, and to discover disease mechanisms and specific disease markers.
GeneAnalytics disease-related outputs rely on the information available in MalaCards, the most comprehensive human disease database.Learn more >
GeneAnalytics enables researchers to identify pathways related to their gene sets, to explore pathway mechanisms and to define gene roles within a pathway.
GeneAnalytics pathway-related outputs rely on the information available in PathCards, the only resource to integrate data from multiple pathway resources into super-pathways.Learn more >
GeneAnalytics enables researchers to identify Gene Ontology (GO) terms related to their gene sets, providing information about the molecular functions and biological roles of the genes of interest.
GeneAnalytics exploits the information available in the Gene Ontology (GO) project, and integrated in GeneCards – the human gene database.Learn more >
GeneAnalytics enables researchers to identify compounds related to their gene sets, and further link to biochemical and pharmacological information about drugs, small molecules and metabolites, their mechanisms of action and their targets.
GeneAnalytics takes advantage of multiple sources of information related to more than 83,000 compounds, including those found in GeneCards, the human gene database.Learn more >
Provides the most valuable results without the need for complex bioinformatics expertise or tools. Developed by biologists, for biologists!
Provides categorized results lists of matched tissues, cells, diseases, pathways, compounds and gene ontology (GO) terms to enhance gene set interpretation.
Results are directly linked to detailed cards in the LifeMap Integrated Biomedical Knowledgebase and to relevant external data sources.
The expression data provided for developing and adult cells, anatomical compartments, organs and tissues in vivo, as well as for stem, progenitor and primary cells in vitro was manually collected, filtered, modeled and integrated.
The expression-based matching algorithm considers gene selectivity, specificity, enrichment and abundance information.