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- 저자 : Suganya Devi (검색결과 2 건)
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In Silico Docking Studies of Selected Flavonoids - Natural Healing Agents against Breast Cancer
Suganya, Jeyabaskar,Radha, Mahendran,Naorem, Devi Leimarembi,Nishandhini, Marimuthu Asian Pacific Journal of Cancer Prevention 2014 Asian Pacific journal of cancer prevention Vol.15 No.19
Background: Breast cancer is the serious health concern in India causing the highest mortality rate in females, which occurs due to uncontrolled cell division and can be metastasize to other parts of the human body. Interactions with estrogen receptor (ER) alpha are mainly responsible for the malignant tumors with regulation of the transcription of various genes as a transcription factor. Most of the drugs currently used for the breast cancer treatment produce various side effects and hence we focused on natural compounds which do not exhibit any toxic effect against normal human cells. Materials and Methods: Structure of human ER was retrieved from the Protein Data Bank and the structures of flavonoid compounds have been collected from PubChem database. Molecular docking and drug likeness studies were performed for those natural compounds to evaluate and analyze the anti-breast cancer activity. Results: Finally two compounds satisfying the Lipinski's rule of five were reported. The two compounds also exhibited highest binding affinity with human ER greater than 10.5 Kcal/mol. Conclusions: The results of this study can be implemented in the drug designing pipeline.
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A machine learning approach for detecting and tracking road boundary lanes
Satish Kumar Satti,Suganya Devi,Prasenjit Dhar,P. Srinivasan 한국통신학회 2021 ICT Express Vol.7 No.1
Road boundary lanes are one of the serious causes of road accidents and it affects the driver and people’s safety. Detecting road boundary lanes is a challenging task for both computer vision and machine learning approaches. In recent years many machine learning algorithms have been deploying but they failed to produce high efficiency and accuracy. This paper presents a novel approach to alert the driver when the car leaps beyond the Road boundary lanes by employing machine learning techniques to avoid road mishaps and ensuring driving safety. Performance is assessed through the generation of experimental results on the dataset. When compared with state-of-the-art lane detection techniques, the proposed technique produced high precision and high efficiency.
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