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Neural Computing and Applications

Erschienen in:

01.05.2007 | Original Article

Ensemble of hybrid neural network learning approaches for designing pharmaceutical drugs

verfasst von: Ajith Abraham, Crina Grosan, Ştefan Ţigan

Erschienen in: Neural Computing and Applications | Ausgabe 3/2007

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Abstract

Designing drugs is a current problem in the pharmaceutical research. By designing a drug we mean to choose some variables of drug formulation (inputs), for obtaining optimal characteristics of drug (outputs). To solve such a problem we propose an ensemble of three learning algorithms namely an evolutionary artificial neural network, Takagi-Sugeno neuro-fuzzy system and an artificial neural network. The ensemble combination is optimized by a particle swarm optimization algorithm. The experimental data were obtained from the Laboratory of Pharmaceutical Techniques of the Faculty of Pharmacy in Cluj-Napoca, Romania. Bootstrap techniques were used to generate more samples of data since the number of experimental data was low due to the costs and time durations of experimentations. Experiment results indicate that the proposed methods are efficient.

Vorheriger Artikel A data reduction approach for resolving the imbalanced data issue in functional genomics

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Titel: Ensemble of hybrid neural network learning approaches for designing pharmaceutical drugs
verfasst von: Ajith Abraham
Crina Grosan
Ştefan Ţigan
Publikationsdatum: 01.05.2007
Verlag: Springer-Verlag
Erschienen in: Neural Computing and Applications / Ausgabe 3/2007
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-007-0090-1

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