Implementation of Computational and Machine learning techniques for the development of in silico tools and identification of novel leads for the treatment of Alzheimer's Disease

dc.contributor.author	Ganeshpurkar, Ankit
dc.date.accessioned	2023-05-26T07:05:20Z
dc.date.available	2023-05-26T07:05:20Z
dc.date.issued	2021
dc.description	This Thesis has been submitted by PHD Scholar IIT(BHU), Varanasi & Supervised by Prof. Sushil K. Singh and Prof. Sushil K. Singh	en_US
dc.description.abstract	Alzheimer's disease (AD) is the most common form of dementia causing memory, behaviour and thinking impairment. Eventually, the symptoms become severe and make it difficult for a patient to carry out daily activities. According to the World Health Organization (WHO), one in every 85 individuals will have AD by 2050. The therapeutic targets of the disease include acetylcholinesterase (AChE), butyrylcholinesterase (BChE), β-secretase-1, glycogen synthase kinase 3β, monoamine oxidase B, matrix metalloproteases, N-methyl D-aspartate (NMDA) receptors, tau kinase etc.	en_US
dc.identifier.other	TH942
dc.identifier.uri	https://idr-sdlib.iitbhu.ac.in/handle/123456789/2392
dc.language.iso	en_US	en_US
dc.publisher	IIT(BHU), Varanasi	en_US
dc.rights.license	CC BY-NC-SA 4.0
dc.subject	Implementation	en_US
dc.subject	Computational	en_US
dc.subject	Machine learning	en_US
dc.subject	techniques	en_US
dc.subject	silico tools	en_US
dc.title	Implementation of Computational and Machine learning techniques for the development of in silico tools and identification of novel leads for the treatment of Alzheimer's Disease	en_US
dc.type	Thesis	en_US