Literature DB >> 8456062

Introduction to backpropagation neural network computation.

R J Erb1.   

Abstract

Neurocomputing is computer modeling based, in part, upon simulation of the structure and function of the brain. Neural networks excel in pattern recognition, that is, the ability to recognize a set of previously learned data. Although their use is rapidly growing in engineering, they are new to the pharmaceutical community. This article introduces neurocomputing using the backpropagation network (BPN).

Mesh：

Year:  1993        PMID： 8456062     DOI： 10.1023/a:1018966222807

Source DB:  PubMed          Journal:  Pharm Res        ISSN： 0724-8741            Impact factor:   4.200


  2 in total

1.  Application of neural computing in pharmaceutical product development.

Authors:  A S Hussain; X Q Yu; R D Johnson
Journal:  Pharm Res       Date:  1991-10       Impact factor: 4.200

2.  On-line prediction of fermentation variables using neural networks.

Authors:  J Thibault; V Van Breusegem; A Chéruy
Journal:  Biotechnol Bioeng       Date:  1990-12-05       Impact factor: 4.530
  2 in total
  13 in total

1.  The use of artificial neural networks for the selection of the most appropriate formulation and processing variables in order to predict the in vitro dissolution of sustained release minitablets.

Authors:  Michael M Leane; Iain Cumming; Owen I Corrigan
Journal:  AAPS PharmSciTech       Date:  2003       Impact factor: 3.246

Review 2.  Modelling and simulation in the development and use of anti-cancer agents: an underused tool?

Authors:  Ferdinand Rombout; Leon Aarons; Mats Karlsson; Anthony Man; France Mentré; Peter Nygren; Amy Racine; Hans Schaefer; Jean-Louis Steimer; Iñaki Troconiz; Achiel van Peer
Journal:  J Pharmacokinet Pharmacodyn       Date:  2004-12       Impact factor: 2.745

Review 3.  Artificial neural network as a novel method to optimize pharmaceutical formulations.

Authors:  K Takayama; M Fujikawa; T Nagai
Journal:  Pharm Res       Date:  1999-01       Impact factor: 4.200

4.  Prediction of pharmacokinetic parameters and the assessment of their variability in bioequivalence studies by artificial neural networks.

Authors:  J Opara; S Primozic; P Cvelbar
Journal:  Pharm Res       Date:  1999-06       Impact factor: 4.200

5.  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.

Authors:  I S Nestorov; S T Hadjitodorov; I Petrov; M Rowland
Journal:  AAPS PharmSci       Date:  1999

Review 6.  The backpropagation neural network--a Bayesian classifier. Introduction and applicability to pharmacokinetics.

Authors:  R J Erb
Journal:  Clin Pharmacokinet       Date:  1995-08       Impact factor: 6.447

7.  Artificial neural network as an alternative to multiple regression analysis in optimizing formulation parameters of cytarabine liposomes.

Authors:  Narayanaswamy Subramanian; Archit Yajnik; Rayasa S Ramachandra Murthy
Journal:  AAPS PharmSciTech       Date:  2004-02-02       Impact factor: 3.246

8.  Pharmacogenomics of drug efficacy in the interferon treatment of chronic hepatitis C using classification algorithms.

Authors:  Wan-Sheng Ke; Yuchi Hwang; Eugene Lin
Journal:  Adv Appl Bioinform Chem       Date:  2010-06-15

9.  Optimization of Salbutamol Sulfate Dissolution from Sustained Release Matrix Formulations Using an Artificial Neural Network.

Authors:  Faith Chaibva; Michael Burton; Roderick B Walker
Journal:  Pharmaceutics       Date:  2010-05-06       Impact factor: 6.321

10.  Quantitative design of regulatory elements based on high-precision strength prediction using artificial neural network.

Authors:  Hailin Meng; Jianfeng Wang; Zhiqiang Xiong; Feng Xu; Guoping Zhao; Yong Wang
Journal:  PLoS One       Date:  2013-04-01       Impact factor: 3.240
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