A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
DAPD: A Knowledgebase for Diabetes Associated Proteins
Tekijät: Gopinath K, Jayakumararaj R, Karthikeyan M
Kustantaja: IEEE COMPUTER SOC
Julkaisuvuosi: 2015
Journal: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Tietokannassa oleva lehden nimi: IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
Lehden akronyymi: IEEE ACM T COMPUT BI
Vuosikerta: 12
Numero: 3
Aloitussivu: 604
Lopetussivu: 610
Sivujen määrä: 7
ISSN: 1545-5963
DOI: https://doi.org/10.1109/TCBB.2014.2359442
Tiivistelmä
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.
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.
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