53results about "Recognition of DNA microarray pattern" patented technology

To provide a method for providing a similar medical image, an image providing system, a program, and a storage medium.SOLUTION: In a method, a feature unit ML-F receives a first medical image IMG-1 for a first patient and applies a first machine learning model ML-1 to the first medical image to determine a first feature vector FV-1. The first machine learning model has been trained on training data sets, each training data set including training medical images and associated non-imaging data. The method also receives a plurality of second feature vectors. The plurality of second feature vectors is a result of applying the first machine learning model to the plurality of second medical images. The method further determines a similar feature vector from the plurality of second feature vectors based on the first feature vector and selects a similar medical image from the plurality of second medical images. Similar medical images are associated with similar feature vectors.SELECTED DRAWING: Figure 4

The invention discloses an immunoblotting image recognition method and system based on deep learning, relates to the field of bioinformatics and computer vision, and solves the problem that it is difficult to extract various related features of protein from an image through an image recognition technology. The method solves the technical problem that a prediction value obtained by predicting a protein expression level obtained by a deep learning model through various protein feature data is difficult to correct. The method comprises the following steps: capturing a strip image on a film by using a high-resolution scanner; recognizing the preprocessed image through an image recognition technology, and extracting features related to the immunoblotting; predicting the feature data through a correspondingly trained deep learning model to obtain an area prediction value, a gray scale difference degree prediction value and a relative brightness prediction value; corresponding weights are given to the three prediction correction values for addition, and a comprehensive prediction correction value is obtained; and comparing the comprehensive expression evaluation value with a preset threshold value to judge the protein expression level.

Image data analysis, and particularly identifying cluster or polony locations for performing base-calling in a digital image of a flow cell during DNA sequencing is described. A method may include generating a first plurality of flow cell images of a cellular sample immobilized on a support by conducting one or more cycles of sequencing reactions. The cellular sample may include a plurality of concatemer molecules therewithin. For the first plurality of flow cell image, pixel intensities, and a respective color purity of each of the pixel intensities may be determined. A base calling template may include base calling locations based on the pixel intensities and the respective color purity of the pixel intensities. The base calling template may be for registering a second plurality of flow cell images of the support in one or more subsequent cycles of the one or more cycles.

The present application discloses a method and apparatus for determining inter-channel crosstalk correction strength, and a medium. The method comprises: detecting multiple cycles of sequencing by synthesis based on a multi-channel microscopic imaging system, such that a first channel signal and a second channel signal are generated in each cycle, wherein a first nucleotide and a second nucleotide are nucleotides comprising different types of bases, and a part of the first channel signal is noise related to second channel crosstalk; detecting the first channel signal and the second channel signal in each cycle, wherein the first channel signal and the second channel signal both have intensity; and performing inter-channel crosstalk correction on the first channel signal intensity by using a predetermined coefficient and the second channel signal intensity to determine the crosstalk-corrected first channel signal intensity, wherein the predetermined coefficient is a fixed coefficient associated with multi-channel microscopic imaging. The present application can correct the inter-channel crosstalk, thereby improving the accuracy and efficiency of the signal correction and / or base calling method.

The application relates to the field of protein deconvolution analysis, in particular to a mass spectrum analysis method and device and an intelligent terminal. The mass spectrum analysis method comprises the following steps: obtaining a target spectrum, wherein the target spectrum is used for reflecting mass spectrum data of a protein peptide segment; determining a spectrum feature of the target spectrum based on a mass-to-charge ratio of the target spectrum; inputting the spectrum feature of the target spectrum into a preset deconvolution model to obtain an analysis category of the target spectrum, wherein the analysis category is used for reflecting a peptide sequence corresponding to the target spectrum. The mass-to-charge ratio of the target spectrum is used to obtain the spectrum feature, and the spectrum feature is input into the deconvolution model, and the analysis category is obtained through the deconvolution model. The application has the characteristics that the peptide sequence of the protein corresponding to the target spectrum can be effectively, accurately and quickly analyzed.

The technology disclosed relates to equalizer-based intensity correction for base calling. In particular, the technology disclosed relates to accessing an image whose pixels depict intensity emissions from a target cluster and intensity emissions from additional adjacent clusters, selecting a lookup table that contains pixel coefficients that are configured to increase a signal-to-noise ratio, applying the pixel coefficients to intensity values of the pixels in the image to produce an output, and base calling the target cluster based on the output.

The present application provides a data processing method and apparatus, a device, and a storage medium, which can be applied to the technical fields of artificial intelligence, bioengineering and the like. The method comprises: acquiring data to be processed, and parsing said data to obtain spatial position information and first feature information of each object in N objects; converting said data into a first image on the basis of the spatial position information and the first feature information of each object; from the first image, extracting second feature information of each object in the N objects; and performing preset processing on the second feature information of each object in the N objects to obtain a processing result of said data. According to the method, data to be processed is converted into a first image, the first image is subjected to feature extraction to obtain second feature information of N objects, and spatial position information is encoded into features; the whole data processing process is simple, a small quantity of computing resources are occupied, and the data processing efficiency is high.

The present invention provides a method for detecting a chromosome copy-number variant (CNV). The method comprises: performing PCA noise reduction and CNV analysis on sequencing data of a sample to be detected, so as to detect whether said sample contains a CNV and / or determine a CNV source, wherein the PCA noise reduction is performed by comparing the sequencing data of said sample with a reference data set, and the reference data set is a principal component feature which represents noise and is obtained after PCA learning is performed on a predetermined sample. According to the method, a CNV can be accurately detected, and the occurrence source of the CNV can be determined, so that false positive or false negative results caused by a maternal CNV in a sample to be detected are effectively avoided, and false detection or missing detection of congenital defects of fetuses is prevented.

The disclosed technique attenuates spatial crosstalk from a sequencing image for base calling. The disclosed technique accesses a section of an image output by a biosensor, the section of the image including a plurality of pixels indicative of intensity radiation values ​​from a plurality of clusters in the biosensor and from locations in the biosensor adjacent to the plurality of clusters. The plurality of clusters includes a target cluster. The section of the image is convolved with a convolution kernel to generate a feature map including a plurality of features having a corresponding plurality of feature values. A weighted feature value is assigned to the target cluster, the weighted feature value being based on one or more of the feature values ​​of the feature map. The weighted feature value assigned to the target cluster is processed to base call the target cluster.

Embodiments of the disclosure include methods for implementing a predictive model that predicts pluripotency of cells through a cost efficient and non-destructive means. The predictive model analyzes contrast images captured from the cells and outputs predictions of cellular pluripotency at the cellular level. Thus, implementation of the predictive model guides the selection and isolation of cells that are predicted to be pluripotent. Furthermore, the predictive model facilitates retrospective analyses to correlate pluripotency metrics with differentiation success and further enables tracking of cellular pluripotency over time (e.g., to evaluate differentiation of cells).

The present disclosure describes systems, non-transitory computer-readable media, and methods for determining adjusted locations of clusters of oligonucleotides based on a phase difference between band-edge spectral regions of a nucleotide-sample-slide region image. For example, the disclosed systems (i) identify a pair of band-edge spectral regions from a spatial frequency band in an oligonucleotide-cluster image and (ii) determine a phase difference between the pair of band-edge spectral regions. Based on the phase difference, the disclosed systems generate a location error in at least one direction for predicted locations of the oligonucleotide clusters. Based on the location error, the disclosed systems determine adjusted locations of the oligonucleotide-clusters within the nucleotide-sample-slide region.

The disclosed technology attenuates spatial crosstalk from sequencing images used for base calling. Specifically, the disclosed technology accesses an image whose pixels depict intensity emissions from a target cluster and intensity emissions from additional neighboring clusters. These pixels include a center pixel that contains a center of the target cluster. Each of these pixels can be divided into a plurality of subpixels. From a particular subpixel, among the plurality of subpixels of the center pixel that contains the center of the target cluster, the disclosed technology selects a subpixel lookup table corresponding to the particular subpixel from a library of subpixel lookup tables. The selected subpixel lookup table contains pixel coefficients configured to maximize a signal-to-noise ratio. The disclosed technology multiplies these pixel coefficients element-wise with the pixels and determines a weighted sum.

Disclosed systems and methods include generating augmented images based on an image, processing, using two or more neural networks, each of the augmented images and the image, determining, for each of the two or more neural networks, a fitness metric, and determining a performance of each of the two or more neural networks based on the determined fitness metric. Disclosed systems and methods also include selecting one or more neural networks, processing, using the selected one or more neural networks, a plurality of images, generating, with the selected one or more neural networks, an association of each image of the plurality of images with one of a plurality of clusters, generating a correlation coefficient, and determining a degree of correlation between a first variant and a second variant of the plurality of variants.

The disclosed technology relates to training a convolutional neural network (CNN) to identify and classify images of segments of an imaging chip that cause process cycle failures. The disclosed technology includes creating a training dataset of images of dimension M×N using labeled images of segments of an imaging chip of dimension J×K. The disclosed technology may fill the M×N frame using horizontal and vertical reflections along edges of the J×K labeled images positioned within the M×N frame. The pre-trained CNN is further trained using the training dataset. The trained CNN may classify the segment images as normal or as depicting a failure. The disclosed technology may train a root cause CNN to classify process cycle images of segments that cause process cycle failures. The trained CNN may classify the segment images by root cause of the process failure among multiple failure categories.

Image data analysis, particularly identifying cluster locations for performing base-calling in a digital flow cell image during DNA sequencing, is described. Each nucleic acid template molecule immobilized on a support may include an insert sequence and a sample index sequence. The sample index sequence may include a k-mer sequence. A sequencing system may conduct k cycles of sequencing reactions of the k-mer sequence before conducting one or more cycles of the insert sequence sequencing reactions and generate a first plurality of flow cell images. Pixel intensities may be determined for pixels of the first plurality of flow cell images. A base calling template may be determined and include base calling locations based on the pixel intensities and respective color purities of the pixel intensities. The base calling template may register a second plurality of flow cell images of the support in one or more cycles subsequent to the k cycles.

The application discloses a kind of stomach cancer HER2 state prediction model training, prediction method and system, wherein, training method includes: obtaining sample image set, the sample image set includes first dyeing image and second dyeing image;Second dyeing image in the sample image set is marked with state, and image label is obtained;The sample image set is pathologically annotated, and training sample set is obtained;The training sample set and the image label are input into untrained stomach cancer HER2 state prediction model and are trained, and the stomach cancer HER2 state prediction model of training completion is obtained.The embodiment of the application can improve the accuracy of stomach cancer HER2 state prediction model, and can be widely applied in artificial intelligence technical field.

A computer-implemented method comprises: determining coefficients corresponding to a section of a flow cell; accessing an image depicting the section of the flow cell and intensity emissions from a target cluster of concatemers; and generating a base call for the target cluster of concatemers by applying the coefficients to the intensity emissions for the target cluster of concatemers.

The disclosed technology attenuates spatial crosstalk from a sequencing image for base calling. In particular, the disclosed technology accesses an image, where pixels of the image indicate intensity radiation from a target cluster and intensity radiation from additional adjacent clusters. The pixels include a central pixel that includes a center of the target cluster. Each pixel within the pixel is divisible into multiple subpixels. Depending on a particular subpixel among the multiple subpixels of the central pixel that includes the center of the target cluster, the disclosed technology selects a subpixel lookup table corresponding to the particular subpixel from a bank of subpixel lookup tables. The selected subpixel lookup table includes pixel coefficients configured to maximize a signal-to-noise ratio. The disclosed technology element-wise multiplies the pixel coefficient and the pixel to determine a weighted sum.

The present invention relates to a method for establishing a correlation between sizes of genetic material contained in virus, virus-like particles (VLPs) or bacteriophages and the measured particle interior intensities of the particles using hydrated state imaging to quantitatively describe, separate and classify the particle populations. The method further comprises the step of graphically displaying the distribution of the particle interior intensities for each reference sample relative to sizes of the genetic material in each reference sample.

The technology disclosed accesses an image whose pixels depict intensity emissions from a target cluster and intensity emissions from additional adjacent clusters. The technology disclosed selects, from a bank of lookup tables, a lookup table that contains pixel coefficients that are configured to increase a signal-to-noise ratio. The technology disclosed applies the pixel coefficients to the pixels having intensity values to produce an output, and base calls the target cluster based on the output.

A mutation detection apparatus includes a memory configured to store software for implementing a neural network and a processor configured to detect a mutation by executing the software, wherein the processor is configured to generate first genome data extracted from a target tissue and second genome data extracted from a normal tissue, extract image data by preprocessing the first genome data and the second genome data, and detect a mutation of the target tissue on the basis of the image data through the neural network trained to correct a sequencing platform-specific false positive.

Methods and systems for image analysis are provided, and in particular for identifying a set of base-calling locations in a flow cell for DNA sequencing. These include capturing flow cell images after each sequencing step performed on the flow cell, and identifying candidate cluster centers in at least one of the flow cell images. Intensities are determined for each candidate cluster center in a set of flow cell images. Purities are determined for each candidate cluster center based on the intensities. Each candidate cluster center with a purity greater than the purity of the surrounding candidate cluster centers within a distance threshold is added to a template set of base-calling locations.

Methods and systems for determining a plurality of sequences of nucleic acid (e.g., DNA) molecules in a sequencing-by-synthesis process are provided. In one embodiment, the method comprises obtaining images of fluorescent signals obtained in a plurality of synthesis cycles. The images of fluorescent signals are associated with a plurality of different fluorescence channels. The method further comprises preprocessing the images of fluorescent signals to obtain processed images. Based on a set of the processed images, the method further comprises detecting center positions of clusters of the fluorescent signals using a trained convolutional neural network (CNN) and extracting, based on the center positions of the clusters of fluorescent signals, features from the set of the processed images to generate feature embedding vectors. The method further comprises determining, in parallel, the plurality of sequences of DNA molecules using the extracted features based on a trained attention-based neural network.

Disclosed systems and methods include generating augmented images based on an image, processing, using two or more neural networks, each of the augmented images and the image, determining, for each of the two or more neural networks, a fitness metric, and determining a performance of each of the two or more neural networks based on the determined fitness metric. Disclosed systems and methods also include selecting one or more neural networks, processing, using the selected one or more neural networks, a plurality of images, generating, with the selected one or more neural networks, an association of each image of the plurality of images with one of a plurality of clusters, generating a correlation coefficient, and determining a degree of correlation between a first variant and a second variant of the plurality of variants.

Methods for utilizing a nucleic acid-based multilevel perceptron are provided.

A method for quantifying labels on a substrate is performed by an electronic device with one or more processors and memory. The method includes obtaining digital data corresponding to a multi-dimensional measurement over the substrate; identifying a first set of sub-portions of the digital data; and, for a respective sub-portion of the first set of sub-portions of the digital data: increasing a quantity of labels, and subtracting a reference signal distribution from the respective sub-portion to obtain subtracted sub-portion data. The method also includes obtaining subtracted digital data. The subtracted digital data includes the subtracted sub-portion data for the respective sub-portion. The method further includes identifying a second set of one or more sub-portions of the subtracted digital data; and, for a respective sub-portion of the second set of one or more sub-portions of the subtracted digital data, increasing a quantity of labels.

The present application discloses a method and apparatus for determining inter-channel crosstalk correction strength, and a medium. The method comprises: detecting multiple cycles of sequencing by synthesis based on a multi-channel microscopic imaging system, such that a first channel signal and a second channel signal are generated in each cycle, wherein a first nucleotide and a second nucleotide are nucleotides comprising different types of bases, and a part of the first channel signal is noise related to second channel crosstalk; detecting the first channel signal and the second channel signal in each cycle, wherein the first channel signal and the second channel signal both have intensity; and performing inter-channel crosstalk correction on the first channel signal intensity by using a predetermined coefficient and the second channel signal intensity to determine the crosstalk-corrected first channel signal intensity, wherein the predetermined coefficient is a fixed coefficient associated with multi-channel microscopic imaging. The present application can correct the inter-channel crosstalk, thereby improving the accuracy and efficiency of the signal correction and / or base calling method.

The invention provides a method for detecting an abnormal area on an image, a method for processing the abnormal area on the image to identify a basic group and related equipment, and is applied to the technical field of sequencing image data processing. According to the method, the abnormal area is identified by analyzing the pixel gray value of the pixel where the position of the nucleic acid template on the to-be-detected image is located and the image block brightness on the to-be-detected image, and the pixel gray value of the position where the nucleic acid template in the abnormal area is located is set to be zero; the type of the base of the nucleic acid template doped into the abnormal region in the sequencing process is identified as unknown, so that abnormal signal interference is eliminated, and the accuracy of base sequencing is improved.

The technology disclosed attenuates spatial crosstalk from sequencing images for base calling. In particular, the technology disclosed accesses an image whose pixels depict intensity emissions from a target cluster and intensity emissions from additional adjacent clusters. The pixels include a center pixel that contains a center of the target cluster. Each pixel in the pixels is divisible into a plurality of subpixels. Depending upon a particular subpixel, in a plurality of subpixels of the center pixel, which contains the center of the target cluster, the technology disclosed selects, from a bank of subpixel lookup tables, a subpixel lookup table that corresponds to the particular subpixel. The selected subpixel lookup table contains pixel coefficients that are configured to maximizes a signal-to-noise ratio. The technology disclosed element-wise multiplies the pixel coefficients with the pixels and determines a weighted sum.

System and methods in this disclosure identify leakage from one partition to surrounding partitions. Detecting leakage may include input data describing the location of the partition, the fluorescence intensity, and whether the partition is positive for an analyte. A machine learning model may be trained on an extensive input data set to identify partitions that have leakage or a high probability of leakage. In some embodiments, features used in the machine learning model include values that describe a neighborhood around the partition and not merely the partition itself.