Database URL: http://nonatobase.ufsc.br/ Internet resources integrating different data related to biodiversity are friendly and widespread nowadays.
Accordingly, the goal was to integrate heterogenous data related to transcriptional regulation into a database in such a way that they fit into a single ontology-based data structure.
IntOGen integrates high-throughput data related to different types of alterations taking place in cancer such as copy number alterations, point mutations and transcriptomic changes from many independent studies to identify the genes and modules (e.g. KEGG pathways, GO terms) more significantly altered in different tumor types (1).
We have addressed this problem by developing a spreadsheet-based data exchange format specification, called ISA-TAB-Nano, which provides a general framework for representing and integrating diverse types of data related to the description and characterization of nanomaterials.
Many of the data in the warehouse originate from databases already owned by the Ministry of Food, Agriculture and Fisheries, but new data will also be integrated in the warehouse, especially data related to welfare.
Briefly, IntegromeDB is a semantic, graph based, 'deep web' data integration system that automatically captures, integrates, and manages publicly available data related to functioning of genes and proteins.
More recently, social scientists have been exploring ways to integrate spatial concepts with social science data related to natural resources for theoretical, practical, and methodological reasons.
We carried out meta-analysis by integrating several sets of gene expression data related to Arabidopsis thaliana pollen development (stages range from UNM, BCP, TCP, HP to 0.5 hr pollen tube and 4 hr pollen tube).
We expect, however, that this assessment can be strongly improved when better pan African monitoring data related to groundwater pollution will be integrated in the assessment methodology.
Here, we approach the process of integrating the available data relating to the relationships between APP and its proteolytic fragments and neuronal processes based on a systems biology approach and build a map of relationships.
