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The abundance of diverse biological data from various sources constitutes a rich source of knowledge, which has the power to advance our understanding of organisms.
The JBioWH framework provides an open-source Java API for integrating biological data from various public databases in a data warehouse manner.
Moreover, these signaling maps can also be overlaid with biological data from various high-throughput experimental platforms to obtain an integrated view of biological processes (3, 4).
Genome browsers provide an aid to the researcher by importing biological data from various sources and presenting these data in an integrated way.
Any attempts to integrate and analyze complex biological data from various "omics" techniques (e.g. transcriptomics, proteomics, and metabolomics) to understand biological phenomena as a collection of interconnected systems will depend on the functional annotation of the majority of their components [ 7].
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But with the diversity of available machines and exposure chambers comes a caveat: how to interpret the biological data generated from various exposure systems?
Consequently, there is a need for the integration of biological data from different sources and of different types in one application, providing various visualization approaches.
This not only includes 6 published YY1 ChIP-seq and ChIP-on-chip data from various biological systems but also 11 recently published datasets from ENCODE (13).
Hence, five biological networks are constructed by collecting data from various databases.
The ND module consists of tables, which are used to store data from various experiments performed on biological entities and also track the relationships of experiments with data stored in other modules.
Dr. Wang introduced NetGestalt, a novel web-based data integration framework that allows simultaneous presentation of large-scale experimental and annotation data from various sources in the context of biological networks to facilitate data visualization, analysis, and interpretation.
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