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3191results about "Hybridisation" patented technology

System and method for cleaning noisy genetic data from target individuals using genetic data from genetically related individuals

ActiveUS20070184467A1Significant resultMicrobiological testing/measurementProteomicsUniparental disomyEmbryo
A system and method for determining the genetic data for one or a small set of cells, or from fragmentary DNA, where a limited quantity of genetic data is available. Genetic data for the target individual is acquired and amplified using known methods, and poorly measured base pairs, missing alleles and missing regions are reconstructed using expected similarities between the target genome and the genome of genetically related subjects. In accordance with one embodiment of the invention, incomplete genetic data from an embryonic cell is reconstructed using the more complete genetic data from a larger sample of diploid cells from one or both parents, with or without genetic data from haploid cells from one or both parents, and/or genetic data taken from other related individuals. In accordance with another embodiment of the invention, incomplete genetic data from a fetus is acquired from fetal cells, or cell-free fetal DNA isolated from the mother's blood, and the incomplete genetic data is reconstructed using the more complete genetic data from a larger sample diploid cells from one or both parents, with or without genetic data from haploid cells from one or both parents, and/or genetic data taken from other related individuals. In one embodiment, the genetic data can be reconstructed for the purposes of making phenotypic predictions. In another embodiment, the genetic data can be used to detect for aneuploides and uniparental disomy.
Owner:NATERA

Combinatorial array for nucleic acid analysis

This invention relates to an array, including a universal micro-array, for the analysis of nucleic acids, such as DNA. The devices and methods of the invention can be used for identifying gene expression patterns in any organism. More specifically, all possible oligonucleotides (n-mers) necessary for the identification of gene expression patterns are synthesized. According to the invention, n is large enough to give the specificity to uniquely identify the expression pattern of each gene in an organism of interest, and is small enough that the method and device can be easily and efficiently practiced and made. The invention provides a method of analyzing molecules, such as polynucleotides (e.g., DNA), by measuring the signal of an optically-detectable (e.g., fluorescent, ultraviolet, radioactive or color change) reporter associated with the molecules. In a polynucleotide analysis device according to the invention, levels of gene expression are correlated to a signal from an optically-detectable (e.g. fluorescent) reporter associated with a hybridized polynucleotide. The invention includes an algorithm and method to interpret data derived from a micro-array or other device, including techniques to decode or deconvolve potentially ambiguous signals into unambiguous or reliable gene expression data.
Owner:CALIFORNIA INST OF TECH

Binary prediction tree modeling with many predictors and its uses in clinical and genomic applications

The statistical analysis described and claimed is a predictive statistical tree model that overcomes several problems observed in prior statistical models and regression analyses, while ensuring greater accuracy and predictive capabilities. Although the claimed use of the predictive statistical tree model described herein is directed to the prediction of a disease in individuals, the claimed model can be used for a variety of applications including the prediction of disease states, susceptibility of disease states or any other biological state of interest, as well as other applicable non-biological states of interest. This model first screens genes to reduce noise, applies k-means correlation-based clustering targeting a large number of clusters, and then uses singular value decompositions (SVD) to extract the single dominant factor (principal component) from each cluster. This generates a statistically significant number of cluster-derived singular factors, that we refer to as metagenes, that characterize multiple patterns of expression of the genes across samples. The strategy aims to extract multiple such patterns while reducing dimension and smoothing out gene-specific noise through the aggregation within clusters. Formal predictive analysis then uses these metagenes in a Bayesian classification tree analysis. This generates multiple recursive partitions of the sample into subgroups (the “leaves” of the classification tree), and associates Bayesian predictive probabilities of outcomes with each subgroup. Overall predictions for an individual sample are then generated by averaging predictions, with appropriate weights, across many such tree models. The model includes the use of iterative out-of-sample, cross-validation predictions leaving each sample out of the data set one at a time, refitting the model from the remaining samples and using it to predict the hold-out case. This rigorously tests the predictive value of a model and mirrors the real-world prognostic context where prediction of new cases as they arise is the major goal.
Owner:DUKE UNIV
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