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  • Open Access


    Deep Learning Based Process Analytics Model for Predicting Type 2 Diabetes Mellitus

    A. Thasil Mohamed, Sundar Santhoshkumar*

    Computer Systems Science and Engineering, Vol.40, No.1, pp. 191-205, 2022, DOI:10.32604/csse.2022.016754

    Abstract Process analytics is one of the popular research domains that advanced in the recent years. Process analytics encompasses identification, monitoring, and improvement of the processes through knowledge extraction from historical data. The evolution of Artificial Intelligence (AI)-enabled Electronic Health Records (EHRs) revolutionized the medical practice. Type 2 Diabetes Mellitus (T2DM) is a syndrome characterized by the lack of insulin secretion. If not diagnosed and managed at early stages, it may produce severe outcomes and at times, death too. Chronic Kidney Disease (CKD) and Coronary Heart Disease (CHD) are the most common, long-term and life-threatening diseases… More >

  • Open Access


    PPARγ LBD and its ligand specificity reveal a selection of potential partial agonist: Molecular dynamics based T2D drug discovery initiative


    BIOCELL, Vol.45, No.4, pp. 953-961, 2021, DOI:10.32604/biocell.2021.015530

    Abstract PPARγ is a peroxisome proliferator-activated receptor (PPAR) family protein and is a target for type 2 diabetes (T2D). In this paper, we have performed a molecular docking analysis between ligand molecules (CID9816265, CID11608015, CID20251380, CID20251343, CID20556263, CID624491, CID42609928, and CID86287562) and PPARγ to determine the ligand specificity. It also helps to understand the ligand-binding domain (LBD) activity of PPARγ during the binding of the ligand. Further, a molecular dynamics simulation study was performed to determine the ligand biding stability in the PPARγ LBD. Its ligand specificity informed us about the potentiality of selecting a partial… More >

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