Application of a pruning algorithm to optimize artificial neural networks for pharmaceutical fingerprinting.
The present study investigates an application of artificial neural networks (ANNs) for use in pharmaceutical fingerprinting. Several pruning algorithms were applied to decrease the dimension of the input parameter data set. A localized fingerprint region was identified within the original input parameter space from which a subset of input parameters was extracted leading to enhanced ANN performance. The present results confirm that ANNs can provide a fast, accurate, and consistent methodology applicable to pharmaceutical fingerprinting.
Department of Biomedical Applications, Institute of Bioorganic and Petroleum Chemistry, Kiev, Ukraine.
I V
IV
Tetko
A E
AE
Villa
T I
TI
Aksenova
W L
WL
Zielinski
J
J
Brower
E R
ER
Collantes
W J
WJ
Welsh
D000465:Algorithms; D002851:Chromatography, High Pressure Liquid; D003201:Computers; D004340:Drug Contamination; D004345:Drug Industry / Q000592:standards; D016571:Neural Networks, Computer; D004364:Pharmaceutical Preparations / Q000032:analysis* / Q000592:standards; D011786:Quality Control
D004364:Pharmaceutical Preparations