358 results about "Genomics" patented technology

Deep learning tools such as convolutional neural networks (CNNs) and transformers have spurred great advancements in computational biology. However, existing methods are constrained architecturally in context length, computational complexity, and model size. This application introduces a sub-quadratic architecture for modeling, which combines projected gated convolutions and structured state spaces to achieve local and global context with, for example, single-nucleotide resolution. These models outperform CNN-, GPT-, BERT-, and long convolution-based models in many tested genomics tasks without pre-training and with 4×-781× fewer parameters. In the proteomics domain, these models similarly outperform pretrained attention-based models, including ESM-1B and TAPE-BERT, on remote homology prediction without pre-training and while using 3,308×-23,636× fewer parameters.

The invention provides a method for drug screening and disease model construction of liver organoid, relates to the technical field of medical research, and adopts multi-modal data acquisition, fusion and intelligent analysis and quantum computing processing to optimize comprehensive application of data, so that the richness, accuracy and analysis efficiency of the data are remarkably improved, and the risk of drug screening and disease model construction of liver organoid is reduced. Multi-source data of genomics, transcriptomics and the like are collected comprehensively, an accurate research system of immune-hepatic disease interaction based on organoid is constructed, an in-vivo complex immune-hepatic disease microenvironment can be highly simulated, the proportion and activation state of immune cells are accurately regulated and controlled in an in-vitro model, functional and phenotypic changes of the immune cells are monitored in real time, and the immune-hepatic disease interaction accuracy is improved. According to the present invention, the interaction mechanism between the immune cells and the liver organ cells is deeply revealed, such that the accurate information basis is provided for the accurate determination of the drug action target, the development of the innovative drug for the immune check point or tumor microenvironment regulation is easily achieved, and the pertinence and the success rate of the drug research and development are substantially improved.

The invention relates to the technical field of molecular biology and molecular markers, in particular to an SNP (Single Nucleotide Polymorphism) molecular marker located on a porcine chromosome 14 and related to porcine reproductive traits and application. The SNP site of the SNP molecular marker located on the No.14 chromosome of the pig and related to the reproductive traits of the pig corresponds to the Cgt at the 105043272th site on the No.14 chromosome of the international pig reference genome version 11.1; t mutation is carried out, and the polymorphism of the basic group at the site affects the porcine reproductive traits; wherein the reproductive performance of pigs with CC and TC genotypes is superior to that of pigs with TT genotypes. By optimizing the dominant alleles of the SNP molecular marker, the frequency of the dominant alleles can be increased generation by generation, the reproductive performance of pigs is improved, and the genetic improvement progress of the pigs is accelerated, so that the economic benefit of pig breeding is effectively improved.

The invention discloses a breast cancer immunohistochemical index and typing prediction method and system. The prediction method comprises the following steps: acquiring a breast ultrasound image and a breast magnetic resonance image of a patient; performing preprocessing and quality control on the mammary gland ultrasonic image and the mammary gland magnetic resonance image; performing breast lesion area segmentation by adopting a deep learning model to obtain segmented lesion areas; based on the segmented lesion area, extracting multi-modal radiomics characteristics of the breast ultrasonic image and the breast magnetic resonance image; fusing the multi-modal radiomics characteristics of the breast ultrasound image and the breast magnetic resonance image, constructing a machine learning model, and performing immunohistochemical index prediction to obtain an immunohistochemical index prediction result; and performing breast cancer molecular typing analysis according to the immunohistochemical index prediction result. By fusing the multi-modal image information, the tumor features can be described more comprehensively, and the accuracy of biomarker prediction is improved.

Described herein are methods such as multi-omic methods for assessing a disease such as cancer. The multi-omic methods may integrate proteomic, transcriptomic, genomic, lipidomic, or metabolomic data. The method screening diseases or disease states. Also described herein are methods for screening for diseases or disease states from biological samples. The methods may include assessing whether a nodule, mass, or cyst is cancerous.

The invention discloses a genomics cross-modal condition generation method and system based on a potential diffusion model, and belongs to the field of bioinformatics, and the method comprises the following steps: respectively designing and constructing an auto-encoder for gene expression and gene mutation data, and carrying out model training; constructing a potential feature space according to an encoder of gene mutation data and training an unconditionally guided potential diffusion model; constructing a cross-modal feature alignment network according to the two trained encoders and training the cross-modal feature alignment network; and performing joint fine tuning on the unconditionally guided potential diffusion model and the cross-modal feature alignment network. According to the method, the problem of sample modal deletion in multi-modal learning is solved, potential correlation between gene expression data and gene mutation data can be captured through cross-modal feature alignment, information flow between different modals is enhanced, and high-quality prediction data is effectively generated when mutation data is deleted through the potential diffusion model.

Methods for assessing disease, such as multi-omics methods, are described herein. A multi-omics method can integrate proteomic data, transcriptomic data, genomic data, lipidomic data, or metabonomic data. The method screens for a disease or a disease state. Also described herein are methods of screening a disease or a disease state from a biological sample. Multi-omics databases and methods of using them are also described herein.

The invention relates to a lymphoma prognosis prediction method and device based on a PET-CT marker and a medium, and the method comprises the following steps: obtaining a PET / CT image of a patient, inputting the PET / CT image into a lymphoma prognosis prediction model based on image-clinical-molecular typing, and obtaining a prognosis risk category; the lymphoma prognosis prediction model based on image-clinical-molecular typing is constructed according to PET-CT markers, clinical indexes and genetic typing data, the PET-CT markers are obtained by calculating a segmentation result of a PET / CT image of a patient according to a bimodal lesion segmentation network, and prognosis significance verification is performed through a K-M survival curve; the bimodal lesion segmentation network comprises an encoder and a decoder, the encoder performs feature extraction through a plurality of convolutional layers, and a channel smoothing layer is introduced after the last convolutional layer to fuse the extracted features. Compared with the prior art, tumor information can be accurately captured from PET / CT images, and lymphoma prognosis prediction is carried out in combination with genomics and clinical medicine.

Presented herein are systems, methods, and non-transient computer readable media for determining predicted response scores of subjects. A computing system may identify a first feature set for a first subject to be administered with immunotherapy to address a condition. The first feature set may include one or more of: (i) a first radiological feature identified in a tomogram of a section associated with the condition in the first subject, (ii) a first immunohistochemistry (IHC) feature derived from an image of a sample associated with the first subject, and (iii) a first genomic feature obtained from gene sequencing of the first subject for genes associated with the condition. The computing system may apply the first feature set to a model. The computing system may determine, from applying the first feature set to the model, a predicted score identifying a response to the immunotherapy to be administered to the first subject.

The invention relates to a method for regulating and controlling gene expression of lactic acid bacteria in shrimp acid fermentation based on systematic biology. The method comprises the following steps: constructing a gene regulation network of lactic acid bacteria by using whole genome data and a high-throughput sequencing technology comprising RNAseq; through clustering analysis and network reconstruction, transcription factors and enzymes of key metabolic pathways of lactic acid generation, acid resistance and glycometabolism are identified, and a dynamic regulation and control model of gene expression is established; a GeneNet or Cytoscape software tool is used, genomics, transcriptomics and metabonomics data of lactic acid bacteria are fused in a multi-layer network, a metabolism regulation and control system is constructed, and the metabolism regulation and control system comprises gene expression regulation and control and real-time change of metabolic flow; the method comprises the following steps: constructing a metabolic flux dynamic model of lactic acid bacteria based on a metabolic flux analysis MFA and a dynamic system modeling technology, and analyzing a mutual relation between a lactic acid generation path and a byproduct generation path; fBA is analyzed through flow equilibrium, and the metabolic network of lactic acid bacteria is optimized.

A system, method and apparatus for executing a bioinformatics analysis on genetic sequence data is provided. Particularly, a genomics analysis platform for executing a sequence analysis pipeline is provided. The genomics analysis platform includes one or more of a first integrated circuit, where each first integrated circuit forms a central processing unit (CPU) that is responsive to one or more software algorithms that are configured to instruct the CPU to perform a first set of genomic processing steps of the sequence analysis pipeline. Additionally, a second integrated circuit is also provided, where each second integrated circuit forming a field programmable gate array (FPGA), the FPGA being configured by firmware to arrange a set of hardwired digital logic circuits that are interconnected by a plurality of physical interconnects to perform a second set of genomic processing steps of the sequence analysis pipeline, the set of hardwired digital logic circuits of each FPGA being arranged as a set of processing engines to perform the second set of genomic processing steps. A shared memory is also provided.

Systems and methods for analyzing multimodal data using one or more large language machine learning models may include inputting medical image data to a trained image-based machine learning model and generating medical image data embeddings; inputting genomic data to a trained genomics machine learning model and generating genomic data embeddings; inputting text data to a trained large language machine learning model, and generating a mapping of extruded data; and feeding the medical image data embeddings, the genomic data embeddings, and the mapping of extruded data to trained large language machine learning model at a foundation layer, the trained large language machine learning model at the foundation layer being trained to generate one or more of a data and analytics report, a diagnostic analysis report, a large language model agent, or user data for display in a user interface at an output device.

The invention relates to the technical field of health management, and discloses a method and system for managing medical health care records of a user, and the method comprises the following steps: S1, collecting multi-modal medical data of the user, including electronic health records, medical image data, vital sign data and genomics data; s2, preprocessing the collected medical data, including data cleaning, format standardization and noise removal; and S3, carrying out time synchronization on the data of different modalities by adopting an optimized time alignment method. According to the method, probability modeling and topological data analysis are adopted, the health prediction precision is improved, false alarms and missing alarms are reduced, federated learning and differential privacy are combined, cross-mechanism collaborative modeling is achieved, data safety is enhanced, health intervention is optimized through reinforcement learning, a management strategy is dynamically adjusted, and the individuation precision is improved; the scheme has better adaptability, safety and intelligence.

The present disclosure provides methods for encoding and decoding signals from barcodes in a plurality of molecular targets from images obtained from imaging based spatial genomics (ISG) experiments. This disclosure sets forth methods, in addition to use of the same, and other solutions to problems in the relevant field.

The invention relates to the technical field of clinical medicine, and discloses an AI prediction system for relapse after radiotherapy of local advanced rectal cancer, and the system comprises a multi-modal biological image database building module which is used for collecting clinical data, image data, radiotherapy target region and dose distribution data, radiobiological data and genomics information of a patient, integrating the data to establish a database; a feature extraction and screening module; a prediction model construction module; a model verification and optimization module; and a clinical application evaluation module. According to the invention, information of patients in multiple aspects such as clinic, image, radiotherapy target area and dose distribution, radiobiology and genomics is widely collected, and the multi-modal biological image database is integrated and established, so that various factors influencing rectum cancer relapse after radiotherapy can be comprehensively covered; a rich, accurate and comprehensive data basis is provided for subsequent accurate analysis and prediction, and the accuracy and reliability of prediction of relapse conditions after radiotherapy are improved.

The invention relates to the field of medical information technology and medical data analysis, in particular to a VTE risk prediction method and system based on multi-modal data fusion and dynamic risk modeling, and the system comprises a first feature extraction module, a second feature extraction module, a two-stage risk assessment module and a final risk judgment module. According to the application, through integration of clinical diagnosis and treatment, iconography and genomics data, a deep learning algorithm, a dynamic weight adjustment mechanism and a graph neural network technology are utilized to realize accurate prediction of the VTE risk of the active-stage lung cancer patient. And meanwhile, the problem of cross-mechanism data distribution difference is solved by adopting transfer learning, and the generalization ability of the model is improved. The method can significantly improve the prediction accuracy, reduces the manual intervention demands, and optimizes the medical resource distribution efficiency.

Various methods and systems for efficiently providing pharmacogenetic guidelines is disclosed. A drug prescription and a patient genotype can be analyzed to provide a drug therapy recommendation. The analysis can include comparing the patient genotype and the drug name to a drug profile database, a molecular biomarker database, hospital drug reaction records and omics databases, and calculating the patient response to the drug.

The invention discloses a method for improving synthesis of sakuranetin from yarrowia lipolytica based on transcriptome analysis and application of the method, and belongs to the technical field of gene engineering. Target genes are analyzed and screened through transcriptome sequencing so as to find a method capable of improving sakuranetin synthesis, and the method specifically comprises the steps that an Illumina HiseqTM platform is used for conducting comprehensive transcriptome analysis on an original strain in the logarithmic phase by applying an RNA-seq technology, then transcriptome data are deeply analyzed through KEGG pathway analysis, and the sakuranetin synthesis can be improved. Therefore, the gene showing significant differential expression in the key path of sakuranetin synthesis is identified. And finally, carrying out free expression and integrated expression on the screened key target gene, and verifying the influence on the sakuranetin yield. Finally, a recombinant yarrowia lipolytica strain which is integrated with the YALI0C01859g gene and has high sakuranetin yield is obtained, and the titer of sakuranetin is increased to 344.0 mg / L.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using an adapted multi-modal sequence generation neural network, e.g., a multi-modal language model neural network, to perform medical tasks. For example, the image encoder neural network of the multi-modal sequence generation neural network can be fine-tuned to encode two-dimensional medical images to allow the neural network to perform medical tasks that require reasoning about two-dimensional medical images. As another example, the video encoder neural network of the multi-modal sequence generation neural network can be fine-tuned to encode volumetric medical images to allow the neural network to perform medical tasks that require reasoning about volumetric medical images. As another example, the image encoder neural network of the multi-modal sequence generation neural network can be fine-tuned to encode non-image medical data, e.g., genomics data, to allow the neural network to perform medical tasks that require reasoning about non-image medical data.

The invention relates to a nuclear magnetic resonance image classification diagnosis method for adult diffuse glioma. The method comprises the following specific steps: step 1, respectively acquiring T2-fluid attenuation inversion recovery T2-FLAIR and contrast enhancement T1 weighted imaging CE-T1WI magnetic resonance images of glioma labeled by a doctor according to histological and genomics information; step 2, carrying out data preprocessing operation on the T2-FLAIR image data and the CE-T1WI image data; step 3, each pair of T2-FLAIR and CE-T1WI images after preprocessing is sent to a decision tree diagnosis model, and an Attention-DenseNet model is used for learning and extracting the characteristics of the T2-FLAIR and CE-T1WI images; 4, obtaining a prediction result based on a plurality of independently trained Attention-DenseNet models, fusing the outputs of the plurality of models by adopting a voting mechanism or a weighted average strategy, and generating a final classification decision; and 5, sending the obtained final classification to a platform, and making final diagnosis based on a classification result.

The present disclosure provides methods, compositions and systems for identifying variant targets of interest, for example, in clinically actionable genes. Disclosed herein are representative assays for identifying gene variants, for example, that are implicated in one or more drug metabolism pathways. The methods, composition and systems disclosed herein enable a highly streamlined and cost-effective workflow for moving forward the emerging field of personalized medicine and related fields of study such as pharmacogenomics.

The invention discloses a radiotherapy sensitivity and toxic and side effect detection system based on multiple omics, and relates to the technical field of radiotherapy, and the system comprises the steps: obtaining genomics data, proteomics data and metabonomics data of a patient at different stages, and generating a patient multi-omics time series data set; based on the patient multi-omics time sequence data set, extracting patient multi-omics time sequence features, and constructing a patient radiotherapy sensitivity dynamic prediction model; combining clinical manifestation and treatment history in clinical data of the patient, generating an adversarial network by utilizing a cGAN condition, generating toxicity simulation data under different radiotherapy doses, and establishing a patient toxic and side effect risk prediction model; and according to the patient radiotherapy sensitivity dynamic prediction model and the toxic and side effect risk prediction model, obtaining an optimal radiotherapy dose interval of the patient, and generating a patient personalized radiotherapy digital twinning scheme. The method has the beneficial effects that the treatment safety and effect of patients are improved, and the method has higher clinical application value and personalized treatment potential.

The invention discloses a resistance gene detection method and system based on metagenomics, and belongs to the field of resistance gene detection.The method comprises the following steps that a genome sequence of microorganisms is constructed based on reads under metagenes; performing species annotation and resistance gene recognition based on the genome sequence of the microorganism to obtain an annotated resistance gene sequence; and analyzing the evolutionary relationship of the resistance gene in the annotated resistance gene sequence, and monitoring and early warning the dynamic change of the expression of the resistance gene based on the evolutionary relationship of the resistance gene and the annotation information of the resistance gene sequence. According to the method, whole-process analysis from original data to distribution, evolution and propagation rules of the resistance genes is realized, and powerful data support is provided for traceability and risk assessment of the resistance genes.

The invention relates to the technical field of genomics, molecular biology, bioinformatics and whole-genome selective breeding, in particular to a whole-genome liquid chip for apostichopus japonicus and application of the whole-genome liquid chip. The liquid phase chip contains sequences of background SNP loci and functional SNP loci which are used for gene analysis and are positioned on an apostichopus japonicus reference genome; wherein the functional SNP sites are associated with important economic characters of the apostichopus japonicus. The invention also discloses application of the liquid chip in apostichopus japonicus genome selective breeding, important economic character gene positioning, genetic diversity analysis, germplasm resource improvement and protection.

The invention provides a disease risk elncRNA and risk gene identification method, equipment, a medium and a program product, and relates to the field of intelligent medical treatment. The invention provides a process SCZ-elnc. Risk elncRNA driven by a disease GWAS signal and a target gene thereof are identified by integrating the following two steps. The method comprises the following steps: (1) identifying risk elncRNA by combining risk SNP, eQTL data and enhancer elements in a genome position; and (2) predicting the gene regulated by the elncRNA through a plurality of support evidences from transcriptomics and epigenomics data.

Fluorescence imaging system designs are described that provide larger fields-of-view, increased spatial resolution, improved modulation transfer and image quality, higher spatial sampling frequency, faster transitions between image capture when repositioning the sample plane to capture a series of images (e.g., of different fields-of-view), and improved imaging system duty cycle, and thus enable higher throughput image acquisition and analysis for genomics and other imaging applications.

The invention relates to the technical field of rectal cancer, and discloses an AI-based local advanced rectal cancer neoadjuvant therapy curative effect prediction system, which comprises a data collection and processing module, a prediction model construction and training module, a prediction model verification and optimization module and a clinical application module, the data collecting and processing module is used for collecting clinical data, image data, radiotherapy target area data, radiotherapy dose data, radiobiology data and genomics data of a patient and then transmitting the data to the prediction model building and training module, and the prediction model building and training module extracts and selects key features in the data. And constructing a tumor segmentation multi-modal fusion and calibration network prediction model based on the PET / CT image. According to the invention, the data of the patient is collected through the data collection and processing module, so that the accuracy of radiotherapy effect and side reaction prediction is improved through the constructed prediction model, and the problem of feature capture limitation caused by a single center and a single mode of an existing AI model is solved.

The present disclosure provides methods for encoding and decoding signals from barcodes in a plurality of molecular targets from images obtained from imaging based spatial genomics (ISG) experiments. This disclosure sets forth methods, in addition to use of the same, and other solutions to problems in the relevant field.

Described herein are methods such as multi-omic methods for assessing a disease such as cancer. The multi-omic methods may integrate proteomic, transcriptomic, genomic, lipidomic, or metabolomic data. The method screening diseases or disease states. Also described herein are methods for screening for diseases or disease states from biological samples. The methods may include assessing whether a nodule, mass, or cyst is cancerous.

Described herein are computer-implemented systems, methods, and devices for pharmacogenomic determination. The system includes a data processor configured to receive pharmacogenomic data representing at least one pharmacogenomic annotation in association with at least one gene; a database configuration engine configured to receive at least one genomic variation of the at least one gene and to search the pharmacogenomic data for at least one association with each genomic variation to return the associated data, the associated data being a haplotype or diplotype and a phenotype; a report generator configured to generate at least one report comprising the associated data with the genomic variation associated.