Advantages of Data Integration of various Databases in GOSTAR
GOSTAR provides a well-established repository of databases, for usage in Discovery & Development. It integrates data from multi-disciplinary areas using Science and Information Technology. It offers an ideal opportunity to search across the complete data while enhancing users’ search experience at the same time. One of the advantages of GOSTAR's is the effective method of finding the desired data for you through a simple search, such as entering the keyword in the related field.
GVKBIO’s GOSTAR aims to provide the most effective database available today by giving access to the complete database. We strive to give you the most knowledgeable product service possible. As you become familiar with GOSTAR on a regular basis, you'll discover hundreds of advantages .Some of them are
- Provides a rapid access to current Knowledge with wide variety of information(More than 250 unique data fields)
- Huge data covered from prestigious journals(List) like Journal of Medicinal Chemistry, Bioorganic Medicinal Chemistry and letters
- Inchy key Algorithm used to identify the unique structures; it will help the user to pull out the data from entire database.
- Domain Indexes created to pullout the data within no time.
- Export the results in the required format like RDF, SDF, CSV, Excel and XML
- Huge set of results can be analyzed in multiple directions like Chemistry/Biological spaces
- Conceptual frame work in GOSTAR to combine the data from different sections like Chemistry, Biology, Pharmacology and Therapeutic Area associated with reference information(Data Source)
- Links to external sites--Few data points will allow the user to connect with public domain databases like NCBI, Swissprot, PUBMED and PDB
- Unique identification numbers across databases
- STR_ID for all structures including metabolic and toxic structures
- Standard_Name_ID for all proteins
- REF_ID for References
- Assay_ID for Bioassay information, Bioassay has been categorized into 15 additional labels based on the content they have and the user will be able to search (very fast) data based on the attribute name
- Followed Therapeutic classification code for Therapeutic Area and Target classification code for proteins
- Physiology based therapeutic classification
- Separated all CSV value fields into individual value fields for faster search in Oracle
- Author to company name mapping
- Seamless integration of reference centric and compound centric databases
- Normalization/standardization of all data from all fields
- Assay values converted to micro-molar concentration enabling a range search over multiple units of measurement
- Extendable/scalable schema that can be integrated with many more related databases