Journal of drug discovery and therapeutics最新文献

筛选
英文 中文
Cognitive Enhancement in Mice: a Data Driven Predictive Model for Evaluating Anxiolytic Effects of Diazepam using Supervised Machinelearning Approach 小鼠的认知能力增强:使用监督机器学习方法评估地西泮抗焦虑作用的数据驱动预测模型
Journal of drug discovery and therapeutics Pub Date : 2023-09-04 DOI: 10.32553/jddt.v11i4.481
Priya Sharma
{"title":"Cognitive Enhancement in Mice: a Data Driven Predictive Model for Evaluating Anxiolytic Effects of Diazepam using Supervised Machinelearning Approach","authors":"Priya Sharma","doi":"10.32553/jddt.v11i4.481","DOIUrl":"https://doi.org/10.32553/jddt.v11i4.481","url":null,"abstract":"In the discipline of pharmacology, where it is vital to investigate natural substances for therapeutic benefits, this study investigates the topic of cognitive enhancement in mice with a focus on the anxiolytic characteristics of diazepam. We present a novel approach to predict and assess the anxiolytic potential of diazepam by combining pharmacology with supervised machine learning and making use of the power of modern data analysis techniques.Machine learning is frequently used to build mathematical models that explain or predict data driven based on previous observations. The support vector regressor, Linear Regression, and naïve Bayesian classifier are perhaps among the most popular supervised algorithms. Behavioral pharmacology, which assesses the behavior of experimental subjects after being injected with various chemicals to see if they have positive or negative effects, is an area of possible application. Diazepam (0.5 and 2 mg/kg) was tested in the elevated plus maze (EPM) in the current investigation to determine its effects. Machine learning techniques (SVR Algorithm) was applied. The results showed an effective anxiolytic effect of the 2 mg/kg dose of diazepam when compared with the control group. The findings of the research using conventional statistical methods indicate that progesterone, at a dose of 2 mg/kg, has an impact that is similar to anxiolytics. The variables that provide additional information to distinguish the experimental groups are automatically identified via machine learning.","PeriodicalId":15565,"journal":{"name":"Journal of drug discovery and therapeutics","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139342930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信