67results about "Recognition of DNA microarray pattern" patented technology

The invention provides a method for evaluating cell population heterogeneity based on an energy index, and belongs to the technical field of biological recognition, and the method comprises the following steps: S1, preprocessing single cell transcriptome data; s2, carrying out feature selection on the preprocessed data, and defining a cell state; s3, constructing a gene local network based on a Hupfield neural network; s4, calculating gene local network energy and cell energy, and carrying out normalization processing on the cell energy; s5, introducing a Gaussian mixture model, identifying the potential state of the cells by analyzing the energy distribution difference of the cells, and clustering the cells in the same differentiation potential state; and step S6, evaluating the heterogeneity of the cell population. By adopting the method for evaluating the heterogeneity of the cell population based on the energy index, the complexity and the difference in the cell population can be disclosed more accurately.

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 invention discloses a gene amplification fluorescence in situ hybridization image intelligent identification system based on deep learning. According to the technical scheme provided by the invention, in the system, an image acquisition module is used for performing image acquisition on a sample marked by a FISH technology by using a microscope to obtain an initial image; the SAM segmentation module is used for carrying out image segmentation on the initial image based on an SAM model to determine a segmented image; the ResNet comparison module is used for comparing each quasi-cell image by using the standard cell image, and topping a real cell image; the fluorescent dot counting module is used for carrying out fluorescent dot identification and counting based on a color filtering technology, and carrying out gene amplification detection based on a target gene signal and a chromosome centromere signal. According to the method, more accurate intelligent segmentation of the FISH image is realized based on the SAM model, the accuracy of cell judgment is further improved by using a deep learning technology, the identification and counting of target gene signals are quickly realized, and the gene amplification level is better judged.

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 technology disclosed relates to training a convolutional neural network (CNN) to identify and classify images of sections of an image generating chip resulting in process cycle failures. The technology disclosed includes creating a training data set of images of dimensions M×N using labeled images of sections of image generating chip of dimensions J×K. The technology disclosed can fill the M×N frames using horizontal and vertical reflections along edges of J×K labeled images positioned in M×N frames. A pretrained CNN is further trained using the training data set. Trained CNN can classify a section image as normal or depicting failure. The technology disclosed can train a root cause CNN to classify process cycle images of sections causing process cycle failure. The trained CNN can classify a section image by root cause of process failure among a plurality of failure categories.

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 invention discloses a base recognition method and system, a gene sequencer and a storage medium. The base recognition method comprises the following steps: acquiring a first image of the biochip in a red light channel and a second image of the biochip in a green light channel (S1); performing base grouping according to the first image and the second image, and preliminarily identifying a base category of each group (S2); adjusting the brightness value of the first image and the brightness value of the second image according to the base categories of all the groups (S3) when the base categories of all the groups are at least two; respectively performing normalization processing on the first image and the second image (S4); and performing base grouping according to the normalized first image and second image, and identifying the base category of each group again (S5). According to the base recognition method, the base category can be accurately recognized for the to-be-sequenced data with partial base category missing, and then the accuracy of gene sequencing can be improved.

Systems and methods are disclosed for processing digital pathology images, prioritizing the digital pathology images, and outputting a sequence of the digital pathology images based on the prioritization. The prioritization may be determined by a machine learning model trained to determine prioritization values based on various criteria. For example, the machine learning may generate biomarker expression information and determine prioritization values based on the generated information.

The invention relates to a method and a system for providing a similar medical image. The methods and the system comprise receiving a first medical image related to a first patient. The methods and the system comprise furthermore comprise determining a first feature vector by applying a first machine learning model to the first medical image. The first machine learning model was trained based on training datasets, each training dataset comprising a training medical image and related non-imaging data. The methods and the system comprise furthermore comprise receiving a plurality of second feature vectors. The plurality of second feature vectors is the result of applying the first machine learning model to a plurality of second medical images. The methods and the system comprise furthermore comprise determining, based on the first feature vector, a similar feature vector from the plurality of second feature vectors. The methods and the system comprise furthermore comprise selecting the similar medical image from the plurality of second medical images, wherein the similar medical image is related to the similar feature vector. The methods and the system comprise furthermore comprise providing the similar medical image.

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.

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.

A method for providing a similar medical image comprises: receiving a first medical image related to a first patient; determining a first feature vector by applying a first machine learning model to the first medical image, the first machine learning model having been trained based on training datasets, and each of the training datasets including a training medical image and related non-imaging data; receiving a plurality of second feature vectors; determining, based on the first feature vector, a similar feature vector from the plurality of second feature vectors; selecting the similar medical image from the plurality of second medical images, wherein the similar medical image is related to the similar feature vector; and providing the similar medical image.

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.

Method for providing a detection output which is representative for the presence of a molecule of interest in a sample, wherein the method is a computer implemented method comprising the steps of: - providing assay data, wherein said assay data comprises a plurality of data values for a plurality of time points being indicative for at least one detection curve obtained from an assay containing said sample; - determining, from said assay data, a value of a second derivative of said detection curve; - comparing said determined value of the second derivative with a predetermined non-zero threshold, and; - providing said detection output if, following from said comparison, said determined value of the second derivative exceeds said threshold.

The invention discloses an image processing device and method based on fluorescence microscope collection, and the method comprises the steps: obtaining a to-be-processed image collected by a fluorescence microscope, and generating a collection environment distribution feature vector of the to-be-processed image; inputting the to-be-processed image and the collection environment distribution feature vector thereof into a pre-trained multi-class image processing model, and outputting a target image corresponding to the to-be-processed image; wherein the pre-trained multi-class image processing model comprises an image attention distribution module, an image distributed processing module and an image feature fusion output module, and the image distributed processing module comprises a plurality of classes of image processing models. Therefore, differential image processing is carried out on the to-be-processed images under different acquisition conditions, and the adaptability and the processing effect of the image processing model are improved.

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.

The invention relates to a method and system for providing similar medical images. Methods and systems include receiving a first medical image associated with a first patient. The method and system also include determining a first feature vector by applying a first machine learning model to the first medical image. The first machine learning model is trained based on training datasets, each training dataset including training medical images and associated non-imaging data. The method and system also include receiving a plurality of second feature vectors. The plurality of second feature vectors are results obtained by applying the first machine learning model to the plurality of second medical images. The method and system also include determining a similar feature vector from the plurality of second feature vectors based on the first feature vector. The method and system also include selecting a similar medical image from the plurality of second medical images, where the similar medical image is related to a similar feature vector. The methods and systems also include providing similar medical images.

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 invention provides a spatial transcriptome permeabilization time analysis method and system, a medium, a product and a terminal. A plurality of biological tissue slice permeabilization images generated based on different permeabilization time are acquired and preprocessed; obtaining target tissue cDNA fluorescence images corresponding to the biological tissue slice permeation images based on an edge detection algorithm according to the preprocessed biological tissue slice permeation images; respectively calculating a brightness index value and a sharpness index value corresponding to the cDNA fluorescence image of each target tissue; and obtaining an optimal permeabilization time analysis image and corresponding optimal permeabilization time according to the brightness index value and the sharpness index value corresponding to each target tissue cDNA fluorescence image. According to the method, the clearest permeabilization condition of the internal organizational structure can be rapidly and automatically interpreted in combination with the image brightness index and the sharpness index, the interpretation accuracy and efficiency are greatly improved, and subjectivity and time consumption of manual judgment are avoided.