scholarly journals Expert System of Anti-Diabetic Medicine Selection

2021 ◽  
Vol 5 (2) ◽  
pp. 175-182
Author(s):  
Andi Hutami Endang
Keyword(s):  
Expert System ◽  
World Health Organization ◽  
Early Stage ◽  
Domain Ontology ◽  
World Health ◽  
Diabetic Medicine ◽  
Knowledge Based ◽  
Health Organization

The Prediction of World Health Organization (WHO) estimates that in 2030 the number of diabetics in Indonesia reached approximately 21.3 million people. Moreover, the development of medicine consumed by diabetics also varies. In this paper, we present a system that represents a diabetes expert into a knowledge based on the domain ontology. The early stage of the system is developing drugs ontology (including functions and contraindications) and patient ontology. Then, matching a weighted ontology will give drugs recommendations that are suitable with the patient's condition. The system is able to analyze diabetes symptoms to give drugs recommendations to the patient.

scholarly journals Knowledge-Based Structural Models of SARS-CoV-2 Proteins and Their Complex with Potential Drugs

2020 ◽  
Author(s):  
Atsushi Hijikata ◽  
Clara Shionyu-Mitsuyama ◽  
Setsu Nakae ◽  
Masafumi Shionyu ◽  
Motonori Ota ◽  
...  
Keyword(s):  
World Health Organization ◽  
Structural Models ◽  
World Health ◽  
Model Quality ◽  
Disease Knowledge ◽  
Knowledge Based ◽  
Experimental Drugs ◽  
The World ◽  
Health Organization ◽  
Natural Medicines

<div>The World Health Organization (WHO) has declared a pandemic of the 2019 novel cornavirus SARS-CoV-2 infection (COVID-19). There is, however, no confirmed anti-COVID-19 therapeutic currently. In order to assist structure-based discovery of repurposing drugs against this disease, knowledge-based models of SARS-CoV-2 proteins were constructed using MODELLER software, and their models were refined by PHENIX and COOT. The model quality was assessed with MolProbity. The ligand molecules in the template structures were compared with approved/experimental drugs and components of natural medicines from the KEGG and KNApSAcK databases. The models suggested several drugs, such as carfilzomib, sinefungin, tecadenoson, and trabodenoson, as potential drugs for COVID-19.</div><div><br></div>

scholarly journals Knowledge-Based Structural Models of SARS-CoV-2 Proteins and Their Complex with Potential Drugs

2020 ◽  
Author(s):  
Atsushi Hijikata ◽  
Clara Shionyu-Mitsuyama ◽  
Setsu Nakae ◽  
Masafumi Shionyu ◽  
Motonori Ota ◽  
...  
Keyword(s):  
World Health Organization ◽  
Structural Models ◽  
World Health ◽  
Model Quality ◽  
Disease Knowledge ◽  
Knowledge Based ◽  
Experimental Drugs ◽  
The World ◽  
Health Organization ◽  
Natural Medicines

<div>The World Health Organization (WHO) has declared a pandemic of the 2019 novel cornavirus SARS-CoV-2 infection (COVID-19). There is, however, no confirmed anti-COVID-19 therapeutic currently. In order to assist structure-based discovery of repurposing drugs against this disease, knowledge-based models of SARS-CoV-2 proteins were constructed using MODELLER software, and their models were refined by PHENIX and COOT. The model quality was assessed with MolProbity. The ligand molecules in the template structures were compared with approved/experimental drugs and components of natural medicines from the KEGG and KNApSAcK databases. The models suggested several drugs, such as carfilzomib, sinefungin, tecadenoson, and trabodenoson, as potential drugs for COVID-19.</div><div><br></div>

World Health Organization Self-Reporting Questionnaire 20--Tigrigna Version

2018 ◽  
Author(s):  
Tesfit Brhane Netsereab ◽  
Meron Mehari Kifle ◽  
Robel Berhane Tesfagiorgis ◽  
Sara Ghebremichael Habteab ◽  
Yosan Kahsay Weldeabzgi ◽  
...  
Keyword(s):  
World Health Organization ◽  
World Health ◽  
Health Organization ◽  
Self Reporting Questionnaire
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