]> BibSonomy publications for /author/Rowland http://www.bibsonomy.org/burst/author/Rowland BibSonomy BuRST Feed for /author/Rowland 2008-07-27T02:44:50+02:00 Cross-Middleware Interoperability in Distributed Concurrent Engineering. dblphttp://www.bibsonomy.org/bibtex/2bf6781710097621188938a3870d94d1d/dblp dblp 2008-07-14T00:00:00+02:00 dblp
Cross-Middleware Interoperability in Distributed Concurrent Engineering.
E. Rowland Watkins and Mark McArdle and Thomas Leonard and Mike Surridge eScience 561-568 (2007)
to dblp by dblp on 2008-07-14 00:00:00 ]]> eScienceconf/eScience/2007561-568Cross-Middleware Interoperability in Distributed Concurrent Engineering.2007dblp 2008-07-14 00:00:00.0 Open Access Citation Rates and Developing Countries. dblphttp://www.bibsonomy.org/bibtex/2af2b0fb37b4fb926d148e396c4bba56e/dblp dblp 2008-06-30T00:00:00+02:00 dblp
Open Access Citation Rates and Developing Countries.
Michael Norris and Charles Oppenheim and Fytton Rowland ELPUB 335-342 (2008)
to dblp by dblp on 2008-06-30 00:00:00 ]]> ELPUBconf/elpub/2008335-342Open Access Citation Rates and Developing Countries.2008dblp 2008-06-30 00:00:00.0 From production to publishing at CJC online: Experiences, insights, and considerations for adoption. dblphttp://www.bibsonomy.org/bibtex/2dcd96742b9b60291d8988b96318b955b/dblp dblp 2008-06-26T00:00:00+02:00 dblp
From production to publishing at CJC online: Experiences, insights, and considerations for adoption.
Michael Felczak and Rowland Lorimer and Richard Smith First Monday 12 (2007)
to dblp by dblp on 2008-06-26 00:00:00 ]]> First Monday10From production to publishing at CJC online: Experiences, insights, and considerations for adoption.122007dblp 2008-06-26 00:00:00.0 Evaluation of Mutual Information and Genetic Programming for Feature Selection in QSAR http://www.bibsonomy.org/bibtex/25ed323ebbeff9cf59306434dde0d25b6/brazovayeye brazovayeye 2008-06-19T17:46:40+02:00 algorithms, genetic programming
Evaluation of Mutual Information and Genetic Programming for Feature Selection in QSAR
Vishwesh Venkatraman and Andrew Rowland Dalby and Zheng Rong Yang Journal of Chemical Information and Modeling 44 1686--1692 (2004)
to algorithms, genetic programming by brazovayeye on 2008-06-19 17:46:40 ]]> Journal of Chemical Information and Modeling51686--1692Evaluation of Mutual Information and Genetic Programming for Feature Selection in {QSAR}442004algorithms, genetic programming 2008-06-19 17:46:40.0Feature selection is a key step in Quantitative Structure Activity Relationship (QSAR) analysis. Chance correlations and multicollinearity are two major problems often encountered when attempting to find generalised QSAR models for use in drug design. Optimal QSAR models require an objective variable relevance analysis step for producing robust classifiers with low complexity and good predictive accuracy. Genetic algorithms coupled with information theoretic approaches such as mutual information have been used to find near-optimal solutions to such multicriteria optimisation problems. In this paper, we describe a novel approach for analyzing QSAR data based on these methods. Our experiments with the Thrombin dataset, previously studied as part of the KDD (Knowledge Discovery and Data Mining) Cup 2001 demonstrate the feasibility of this approach. It has been found that it is important to take into account the data distribution, the rule {"}interestingness{"}, and the need to look at more invariant and monotonic measures of feature selection. On Genetic Programming and Knowledge Discovery in Transcriptome Data http://www.bibsonomy.org/bibtex/27d52d9454ee28d82eb1fe876b70a545d/brazovayeye brazovayeye 2008-06-19T17:46:40+02:00 Bioinformatics Biology Computation Computational Evolutionary algorithms, and genetic in programming,
On Genetic Programming and Knowledge Discovery in Transcriptome Data
Jem Rowland Proceedings of the 2004 IEEE Congress on Evolutionary Computation 158--165 (2004)
to Bioinformatics Biology Computation Computational Evolutionary algorithms, and genetic in programming, by brazovayeye on 2008-06-19 17:46:40 ]]> Portland, OregonProceedings of the 2004 IEEE Congress on Evolutionary Computation20-23 June158--165On Genetic Programming and Knowledge Discovery in Transcriptome Data2004Bioinformatics Biology Computation Computational Evolutionary algorithms, and genetic in programming, 2008-06-19 17:46:40.0This paper concerns the use of Genetic Programming (GP) for supervised classification of transcriptome (gene expression) data. In such applications GP can produce accurate predictive models that generalize well and use only very few gene expression values. It is often suggested that the selected genes are therefore of biological significance in discriminating the classes. The paper presents a preliminary study of successful parsimonious GP models to investigate the extent to which the selected variables contribute to the classification. The work is based on a readily available and well studied dataset that represents gene expression values for two groups of patients with different forms of Leukaemia. Model selection methodology in supervised learning with evolutionary computation http://www.bibsonomy.org/bibtex/2f2a37a20fc3d7df4221fc6f52e8e5bbd/brazovayeye brazovayeye 2008-06-19T17:46:40+02:00 Gene Generalization, Model Validation algorithms, expression, genetic programming, selection,
Model selection methodology in supervised learning with evolutionary computation
J. J. Rowland Biosystems 72 187--196 (2003)
to Gene Generalization, Model Validation algorithms, expression, genetic programming, selection, by brazovayeye on 2008-06-19 17:46:40 ]]> BiosystemsNovember1-2187--196Model selection methodology in supervised learning with evolutionary computation722003Gene Generalization, Model Validation algorithms, expression, genetic programming, selection, 2008-06-19 17:46:40.0The expressive power, powerful search capability, and the explicit nature of the resulting models make evolutionary methods very attractive for supervised learning applications in bioinformatics. However, their characteristics also make them highly susceptible to overtraining or to discovering chance relationships in the data. Identification of appropriate criteria for terminating evolution and for selecting an appropriately validated model is vital. Some approaches that are commonly applied to other modeling methods are not necessarily applicable in a straightforward manner to evolutionary methods. An approach to model selection is presented that is not unduly computationally intensive. To illustrate the issues and the technique two bioinformatic datasets are used, one relating to metabolite determination and the other to disease prediction from gene expression data. Interpreting Analytic Spectra with Evolutionary Computation http://www.bibsonomy.org/bibtex/2fd76c1c45a3603bc6bf8e4740164d75b/brazovayeye brazovayeye 2008-06-19T17:46:40+02:00 algorithms, genetic programming
Interpreting Analytic Spectra with Evolutionary Computation
Jem J. Rowland Evolutionary Computation in Bioinformatics 341--365 (2003)
to algorithms, genetic programming by brazovayeye on 2008-06-19 17:46:40 ]]> San Francisco, USAEvolutionary Computation in Bioinformatics16341--365Interpreting Analytic Spectra with Evolutionary Computation2003algorithms, genetic programming 2008-06-19 17:46:40.0 Adaptive Denoising in Spectral Analysis by Genetic Programming http://www.bibsonomy.org/bibtex/2a933ba93bace8b1bb26d57f1d9351c2f/brazovayeye brazovayeye 2008-06-19T17:46:40+02:00 algorithms, genetic programming
Adaptive Denoising in Spectral Analysis by Genetic Programming
Jem J. Rowland and Janet Taylor Proceedings of the 2002 Congress on Evolutionary Computation CEC2002 133--138 (2002)
to algorithms, genetic programming by brazovayeye on 2008-06-19 17:46:40 ]]> Proceedings of the 2002 Congress on Evolutionary Computation CEC2002133--138Adaptive Denoising in Spectral Analysis by Genetic Programming2002algorithms, genetic programming 2008-06-19 17:46:40.0 Spectral Analysis via Supervised Genetic Search with Application-specific Mutations http://www.bibsonomy.org/bibtex/2af130d0d73f4dccfb7085eff94c8b64c/brazovayeye brazovayeye 2008-06-19T17:35:00+02:00 algorithms, biotechnology, calibration genetic programming, spectroscopy, supervised,
Spectral Analysis via Supervised Genetic Search with Application-specific Mutations
Janet Taylor and Jem J. Rowland and Douglas B. Kell Proceedings of the 2001 Congress on Evolutionary Computation CEC2001 481--486 (2001)
to algorithms, biotechnology, calibration genetic programming, spectroscopy, supervised, by brazovayeye on 2008-06-19 17:35:00 ]]> COEX, World Trade Center, 159 Samseong-dong, Gangnam-gu, Seoul, KoreaProceedings of the 2001 Congress on Evolutionary Computation CEC200127-30 May481--486Spectral Analysis via Supervised Genetic Search with Application-specific Mutations2001algorithms, biotechnology, calibration genetic programming, spectroscopy, supervised, 2008-06-19 17:35:00.0 Rapid and Quantitative Detection of the Microbial Spoilage of Meat by Fourier Transform Infrared Spectroscopy and Machine Learning http://www.bibsonomy.org/bibtex/263da4b0cda7be6e9e46c27dd3bf23c5c/brazovayeye brazovayeye 2008-06-19T17:35:00+02:00 algorithms, genetic programming
Rapid and Quantitative Detection of the Microbial Spoilage of Meat by Fourier Transform Infrared Spectroscopy and Machine Learning
David I. Ellis and David Broadhurst and Douglas B. Kell and Jem J. Rowland and Royston Goodacre Applied and Environmental Microbiology 68 2822--2828 (2002)
to algorithms, genetic programming by brazovayeye on 2008-06-19 17:35:00 ]]> Applied and Environmental MicrobiologyJune62822--2828Rapid and Quantitative Detection of the Microbial Spoilage of Meat by Fourier Transform Infrared Spectroscopy and Machine Learning682002algorithms, genetic programming 2008-06-19 17:35:00.0Fourier transform infrared (FT-IR) spectroscopy is a rapid, noninvasive technique with considerable potential for application in the food and related industries. We show here that this technique can be used directly on the surface of food to produce biochemically interpretable ?fingerprints.? Spoilage in meat is the result of decomposition and the formation of metabolites caused by the growth and enzymatic activity of microorganisms. FT-IR was exploited to measure biochemical changes within the meat substrate, enhancing and accelerating the detection of microbial spoilage. Chicken breasts were purchased from a national retailer, comminuted for 10 s, and left to spoil at room temperature for 24 h. Every hour, FT-IR measurements were taken directly from the meat surface using attenuated total reflectance, and the total viable counts were obtained by classical plating methods. Quantitative interpretation of FT-IR spectra was possible using partial least-squares regression and allowed accurate estimates of bacterial loads to be calculated directly from the meat surface in 60 s. Genetic programming was used to derive rules showing that at levels of 10 7 bacteria.g 1 the main biochemical indicator of spoilage was the onset of proteolysis. Thus, using FT-IR we were able to acquire a metabolic snapshot and quantify, noninvasively, the microbial loads of food samples accurately and rapidly in 60 s, directly from the sample surface. We believe this approach will aid in the Hazard Analysis Critical Control Point process for the assessment of the microbiological safety of food at the production, processing, manufacturing, packaging, and storage levels.