Bioinformatics and Computational Biology Solutions Using R and Bioconductor (Statistics for Biology and Health)
Microarray blob-defect removal improves array analysis
Bioinformatics
Creating regular expressions as mRNA motifs with GP to predict human exon splitting
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Distilling GeneChips with GP on the emerald GPU supercomputer
ACM SIGEVOlution
Correlation of microarray probes give evidence for mycoplasma contamination in human studies
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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Modern biology has moved from a science of individual measurements to a science where data are collected on an industrial scale. Foremost, among the new tools for biochemistry are chip arrays which, in one operation, measure hundreds of thousands or even millions of DNA sequences or RNA transcripts. While this is impressive, increasingly sophisticated analysis tools have been required to convert gene array data into gene expression levels. Despite the assumption that noise levels are low, since the number of measurements for an individual gene is small, identifying which signals are affected by noise is a priority. High-density oligonucleotide array (HDONAs) from NCBI GEO shows that, even in the best Human GeneChips 1/4 percent of data are affected by spatial noise. Earlier designs are noisier and spatial defects may affect more than 25 percent of probes. BioConductor R code is available as supplementary material which can be found on the Computer Society Digital Library at http://doi.ieeecomputersociety.org/10.1109/TCBB.2008.108 and via http://bioinformatics.essex.ac.uk/users/wlangdon/TCBB-2007-11-0161.tar.gz.