62results about "Recognition of DNA microarray pattern" patented technology

The invention discloses an apparatus and method for automatic detection of pathogens within a sample. The apparatus and method are especially useful for high-throughput and/or low-cost detection of parasites with minimal need for trained personnel. The invention entails automated microscopic data acquisition, and performing image processing of images captured from a sample using classification algorithms. Visual classification features of structures are extracted from the image and compared to visual classification features associated with known pathogens. A determination is reached whether a pathogen is present in the sample; and if present, the pathogen may be identified to a pathogen species. Diagnosis is rapid and does not require medically trained personnel.

Feature locations are determined in an array image obtained from reading a chemical array comprising multiple features arranged in a polygon and which array optionally includes sub-arrays within the array. A complete image of the array or a sub-array is presented on a display. Magnified images of corner regions of the displayed complete image are simultaneously presented on the display with the complete image. A user selection of corners for the array or sub-array are received and an array or sub-array actual outline is generated by linearly connecting the user selected corners. The generated actual outline is presented on the magnified image and may also be simultaneously presented on the complete image. Feature locations in the array or sub-array image may be determined based on the actual outline.

A digital image processing-based system and method for quantitatively processing a plurality of nucleic acid species expressed in a microarray are disclosed. The microarray is a grid of a plurality of sub-grids of the nucleic acid species. The system includes a scanner that has a digital scanning sensor that scans the microarray and transmits from an output a digital image of the microarray, and a computer that receives the digital image of the microarray from the scanner and then processes the digital image, identifying each of the microarray's sub-grids. The computer identifies the position of each of the sub-grids by (a) identifying regions in the digital image that each contains one of the sub-grids, (b) identifying rows and columns in each region where nucleic acid species are expressed to form a set of candidate sub-grids in each region, (c) selecting for each region a probable sub-grid from the set of candidate sub-grids in each region, and (d) comparing the positions of the probable sub-grids from each region to finalize the sub-grid positions.

A method (and system) for using information contained within the scanned image to create, in an automated (or semi-automated) process, an accurate data grid. The process has steps: enhance the image; locate blocks of spots; and find each individual spot in each of the blocks. Preferably, the method makes use of image filtering using a“Principal Frequency Filter” based on a mathematical determination of major periodic elements in the image to eliminate noisy, non-periodic signals, and of smoothed intensity profiles of the filtered image data. Here, the term Principal Frequency Filter is used to indicate an image-enhancing filter based upon a mathematical operation which identifies the major periodic components of the image.

Apparatus, systems and methods for determining, for each respective phenotypic characterization in a set of {T₁, . . . , T_k} characterizations, that a test specimen has the respective characterization are provided. A pairwise probability function g_pq(X, W_pq), for a phenotypic pair (T_p, T_q) in {T₁, . . . , T_k} is learned using a training population. W_pq is a set of parameters derived from Y for (T_p, T_q) by substituting each Y₁ in Y into g_pq(X, W_pq), as X, where Y_i is the set of cellular constituent abundance values from sample i in the training population exhibiting T_p or T_q. The learning step is repeated for each (T_p, T_q) in {T₁ . . . , T_k}, thereby deriving pairwise probability functions G={g_1,2(X, W_1,2), . . . , g_{k-1, k}(X, W_{k-1, k})}. Pairwise probability values P={p_1,2, . . . , p_{k-1, k}} are computed, where each p_pq is equal to g_pq(Z, W_pq) in G, the probability that the test specimen has T_p and not T_q, where Z is cellular constituent abundance values of the test specimen.

A method for evaluating an orientation of a molecular array having features arranged in a pattern. An image of the molecular array is obtained by scanning the molecular array to determine data signals emanating from discrete positions on a surface of the molecular array. An actual result of a function on pixels of the image which pixels lie in a second pattern, is calculated. This actual result is compared with an expected result which would be obtained if the second pattern had a predetermined orientation on the array. Array orientation can then be evaluated based on the result.

Disclosed are devices and methods for collection and analyzing biological samples containing nucleic acid in conjunction with collecting at least one ridge and valley signature of a test subject, while keeping the sample and signature associated with each other. Such devices and methods are used in forensic, human identification, screening, and access control technologies to rapidly process an individual's identity or determine the identity of an individual.

The invention provides a chromosome karyotype image intelligent evaluation method and system. The method relates to the technical field of image processing, and is characterized in that automatic labeling of chromosome image data is carried out through a weak supervised learning model; annotation costs is reduced, labeling efficiency is improved, extension modes such as random projection transformation are adopted for chromosome image data; an optimized residual network model is adopted to build a scoring network model, detail differences of chromosome images are better reserved, the accuracyof the scoring network model is improved, meanwhile, a scoring system is established by adopting a micro-service distributed frame through a cloud platform technology, cross-cloud image analysis is achieved, and the efficiency of the system is improved.

The invention is applicable to the technical field of data mining and provides a method and a device for classifying chromosome sequences and plasmid sequences. The method comprises steps: chromosome sequences and plasmid sequences are acquired, and a first training sample and a second training sample are obtained; frequency characteristics of all k character short strings and reverse complementary sequence pairs thereof are extracted and a first frequency characteristic table and a second frequency characteristic table are generated, wherein k is no less than 2 but no more than 5; a training set and a test set are extracted from the first frequency characteristic table and the second frequency characteristic table, and a chi-square test algorithm is adopted to calculate weight values of all characteristic data in the training set; a random forests algorithm is adopted and according to the characteristic data whose weight values meet preset conditions, a classification model is trained; and according to the classification model, the chromosome sequences and the plasmid sequences are classified. Thus, the training efficiency and the training effects of the classification model are improved, and accuracy on classification on the chromosome sequences and the plasmid sequences is improved.

A method and system for automatically processing images from a micro-array uses an image processor that is controlled by software. The software controls the microprocessor to process images to ensure that the images meet a predetermined threshold. The microprocessor automatically calculates a size of spot image, a spacing between adjacent spot images, generates a first grid and adjusts the first grid to fit the spot images being processed.