F
Frank R. Burden
Researcher at Commonwealth Scientific and Industrial Research Organisation
Publications - 67
Citations - 3796
Frank R. Burden is an academic researcher from Commonwealth Scientific and Industrial Research Organisation. The author has contributed to research in topics: Artificial neural network & Quantitative structure–activity relationship. The author has an hindex of 30, co-authored 67 publications receiving 3352 citations. Previous affiliations of Frank R. Burden include Flinders University & Monash University.
Papers
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Book ChapterDOI
Bayesian regularization of neural networks.
Frank R. Burden,David A. Winkler +1 more
TL;DR: Bayesian regularized artificial neural networks (BRANNs) as mentioned in this paper are more robust than standard back-propagation nets and can reduce or eliminate the need for lengthy cross-validation.
Journal ArticleDOI
Quantitative Structure–Property Relationship Modeling of Diverse Materials Properties
TL;DR: Quantitative Structure Property Relationship Modeling of Diverse Materials Properties Tu Le, V. Chandana Epa, Frank R. Burden, and David A. Winkler.
Journal ArticleDOI
Molecular identification number for substructure searches
TL;DR: A method for producing molecular identification numbers for hydrogen-depleted organic structures from the eigenvalues of a connectivity matrix is presented.
Journal ArticleDOI
Robust QSAR models using Bayesian regularized neural networks.
Frank R. Burden,David A. Winkler +1 more
TL;DR: Bayesian regularized artificial neural networks (BRANNs) have the potential to solve a number of problems which arise in QSAR modeling such as: choice of model; robustness of models; choice of validation set; size of validation effort; and optimization of network architecture.
Journal ArticleDOI
Fourier Transform Infrared microspectroscopy and chemometrics as a tool for the discrimination of cyanobacterial strains
TL;DR: F Fourier Transform Infrared (FTIR) microspectroscopy, in combination with chemometrics, was investigated as a novel method to discriminate between cyanobacterial strains and showed that the two strains of Microcystis aeruginosa exhibited the highest degree of similarity, while the eukaryotic taxon was the most dissimilar from the prokaryoticTaxa.