Empirical versus mechanistic modelling: Comparison of an artificial neural network to a mechanistically based model for quantitative structure pharmacokinetic relationships of a homologous series of barbiturates
Author:
Publisher
Springer Science and Business Media LLC
Subject
Pharmaceutical Science
Link
http://link.springer.com/content/pdf/10.1208/ps010417.pdf
Reference39 articles.
1. Thakur AK, Model mechanistic vs empirical. In: Rescigno A, Thakur AK, eds. New Trends in Pharmacokineties. New York: Plenum Press. 1991; 41–51.
2. Rescigno A, Beck JS (Comments: Thakur AK). The use and abuse of models. I Pharmacokinet Biopharm. 1987;15:327.
3. Veng-Pedersen P, Modi NB. Neural networks in pharmacodynamic modeling: is current modeling practice of complex kinetic systems at a dead end? J Pharmacokinet Biopharm. 1992;20:397–412.
4. Siegel RA. Commentary on “Neural networks in pharmacodynamic modeling: is current modeling practice of complex kinetic systems at a dead end?” J Pharmacokinet Biopharm. 1992;20:413–416.
5. Veng-Pedersen P. Response to Siegel's commentary. J Pharmacokinet Biopharm. 1992;20:417–418.
Cited by 22 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Continuous direct compression: Development of an empirical predictive model and challenges regarding PAT implementation;International Journal of Pharmaceutics: X;2022-12
2. Mathematical Modelling of an Application Specific Processor Architecture with Power Optimization;2021 IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering (WIECON-ECE);2021-12-04
3. Research on Artificial Neural Networks in Bulgarian Academy of Sciences;Studies in Computational Intelligence;2021
4. Role of pharmacokinetic consideration for the development of drug delivery systems: A historical overview;Advanced Drug Delivery Reviews;2020
5. Prediction of Drug Distribution in Rat and Humans Using an Artificial Neural Networks Ensemble and a PBPK Model;Pharmaceutical Research;2014-05-28
1.学者识别学者识别
2.学术分析学术分析
3.人才评估人才评估
"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370
www.globalauthorid.com
TOP
Copyright © 2019-2024 北京同舟云网络信息技术有限公司 京公网安备11010802033243号 京ICP备18003416号-3