Literature DB >> 26357271

DAPD: A Knowledgebase for Diabetes Associated Proteins.

Krishnasamy Gopinath, Ramaraj Jayakumararaj, Muthusamy Karthikeyan.   

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.

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Year:  2015        PMID: 26357271     DOI: 10.1109/TCBB.2014.2359442

Source DB:  PubMed          Journal:  IEEE/ACM Trans Comput Biol Bioinform        ISSN: 1545-5963            Impact factor:   3.710


  2 in total

1.  Polyvalent therapeutic vaccine for type 2 diabetes mellitus: Immunoinformatics approach to study co-stimulation of cytokines and GLUT1 receptors.

Authors:  Syed Aun Muhammad; Hiba Ashfaq; Sidra Zafar; Fahad Munir; Muhammad Babar Jamshed; Jake Chen; Qiyu Zhang
Journal:  BMC Mol Cell Biol       Date:  2020-07-23

2.  On building a diabetes centric knowledge base via mining the web.

Authors:  Fan Gong; Yilei Chen; Haofen Wang; Hao Lu
Journal:  BMC Med Inform Decis Mak       Date:  2019-04-09       Impact factor: 2.796

  2 in total

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