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Patent Watch: Dec 16, 2008

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Luminex of Austin, Tex., has received US Patent No. 7,465,540, “Multiple reporter read-out for bioassays.” The patent claims a method for detecting reactive sites on an analyte, by allowing reactants on an addressable microsphere and the reactive sites to react, forming reactant-reactive site pairs distinguishable by fluorescence intensity. The patent also provides a method for detecting analytes in a sample using addressable microspheres in combination with one or more reporter reagents. Additionally, a method for determining allele zygosity of a genetic locus having two alleles or more alleles using microparticles, and a method for detecting a plurality of SNPs in nucleic acid molecules, is claimed.
 

 
Vialogy of Altadena, Calif., has received US Patent No. 7,466,851, “Technique for extracting arrayed data.” The patent describes a spectral transformation technique for characterizing digitized intensity output patterns from microarrays. The technique includes the steps of extracting pixels associated with an object of interest, and transforming the pixels from an intensity representation to a spectral representation. In some embodiments, the extraction is based on a tessellated logarithmic spiral extraction that may yield a pixel core with a sampling of both foreground and background pixels. This core may then be computationally rescaled to enhance spatial resolution. Once the extracted pixels are represented in the spectral regime, convolution with resolution-enhancement kernels may be used to accentuate morphological features capturing platform specific phenomenology, the patent states.
 

 
Illumina has received US Patent No. 7,467,117, “Artificial intelligence and global normalization methods for genotyping.” The patent claims a method of genetic analysis. The method includes a) carrying out instructions on a computer system obtaining genetic data comprising n sets of first and second signal values related in a coordinate system, where said first and second signal values are indicative of the levels of a first and second allele, respectively, at n loci, where n is an integer greater than one; b) comparing the fit of the genetic data to each of a number of cluster models using an artificial neural network, thereby determining a best-fit cluster model; c) assigning signal values to at least one cluster according to the best fit cluster model, if the best-fit cluster model contains at least one actual cluster and at least one missing cluster; d) using a second artificial neural network to create a proposed location for at least one missing cluster; and e) analyzing genetic data using the cluster model, where the analysis is performed for determining a genotype based on said genetic data.

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