Data mining techniques are used to find interesting patterns for medical diagnosis and treatment. Diabetes is a group of metabolic disease in which there are high blood sugar levels over a prolonged period. This paper concentrates on the overall literature survey related to various data mining techniques for predicting diabetes. This would help the researchers to know various data mining algorithm and method for the prediction of diabetes mellitus.
Hepatitis C, the silent disease caused by Hepatitis C virus (HCV) is a chronic health infection globally. HCV causes permanent hepatic cirrhosis and carcinoma in humans. WHO estimated about 3 million incidents of HCV infection around the world. Multiple variant genotypes along with the development of Quasi-species limited the efficacy of drugs used for the treatment of HCV infections. This heterogeneity of the virus hampered the drug development against them. The virus hosts many structural and non-structural (NS) proteins. NS5B is a non-structural protein with a unique structure and function. The protein is a RNA dependent RNA polymerase (RdRp) responsible for building the vital genetic component of the virus. Inhibition of NS5B stops viral replication and propagation. The major role played by RdRP makes it a preferential target for anti-HCV drug development. An association of docking and rescoring studies was performed to 24 compounds derived from various plant sources to estimate their activity against HCV NS5B RdRp. Based on the docking characterization and ADMET properties andrographolide, esculetin, columbin and tinosporide were identified as they showed greater potency against HCV NS5B RdRp. However, based on hepato bioactive spectrum and ADMET score, andrographolide from Andrographuis paniculata emerged as a strong contender with lead like characteristics acting as a promising drug candidate.