DocumentCode
56965
Title
DAPD: A Knowledgebase for Diabetes Associated Proteins
Author
Gopinath, Krishnasamy ; Jayakumararaj, Ramaraj ; Karthikeyan, Muthusamy
Author_Institution
Dept. of Bioinf., Alagappa Univ., Karaikudi, India
Volume
12
Issue
3
fYear
2015
fDate
May-June 1 2015
Firstpage
604
Lastpage
610
Abstract
Recent advancements in genomics and proteomics provide a solid foundation for understanding the pathogenesis of diabetes. Proteomics of diabetes associated pathways help to identify the most potent target for the management of diabetes. The relevant datasets are scattered in various prominent sources which takes much time to select the therapeutic target for the clinical management of diabetes. However, additional information about target proteins is needed for validation. This lacuna may be resolved by linking diabetes associated genes, pathways and proteins and it will provide a strong base for the treatment and planning management strategies of diabetes. Thus, a web source “Diabetes Associated Proteins Database (DAPD)” has been developed to link the diabetes associated genes, pathways and proteins using PHP, MySQL. The current version of DAPD has been built with proteins associated with different types of diabetes. In addition, DAPD has been linked to external sources to gain the access to more participatory proteins and their pathway network. DAPD will reduce the time and it is expected to pave the way for the discovery of novel anti-diabetic leads using computational drug designing for diabetes management. DAPD is open accessed via following url www.mkarthikeyan.bioinfoau.org/dapd.
Keywords
bioinformatics; database management systems; diseases; drugs; genetics; genomics; medical computing; medical disorders; proteins; proteomics; DAPD; MySQL; PHP; clinical management; computational drug; diabetes associated genes; diabetes associated proteins; diabetes associated proteins database; diabetes pathogenesis; genomics; lacuna; planning management strategy; proteomics; treatment management strategy; Bioinformatics; Computational biology; Databases; Diabetes; Diseases; Drugs; Proteins; Bioinformatics (protein) databases; Biology and genetics; Database design; Diabetes; bioinformatics (protein) databases; database design; diabetes;
fLanguage
English
Journal_Title
Computational Biology and Bioinformatics, IEEE/ACM Transactions on
Publisher
ieee
ISSN
1545-5963
Type
jour
DOI
10.1109/TCBB.2014.2359442
Filename
6966775
Link To Document