4598results about "Hybridisation" patented technology

The invention provides methods, systems, and computer readable medium for detecting ploidy of chromosome segments or entire chromosomes, for detecting single nucleotide variants and for detecting both ploidy of chromosome segments and single nucleotide variants. In some aspects, the invention provides methods, systems, and computer readable medium for detecting cancer or a chromosomal abnormality in a gestating fetus.

The invention provides methods, systems, and computer readable medium for detecting ploidy of chromosome segments or entire chromosomes, for detecting single nucleotide variants and for detecting both ploidy of chromosome segments and single nucleotide variants. In some aspects, the invention provides methods, systems, and computer readable medium for detecting cancer or a chromosomal abnormality in a gestating fetus.

The invention discloses a genomics-based Parkinson's disease drug target prediction model construction method, and relates to the technical field of drug research and development, and the method comprises the following steps: collecting genomics, transcriptomics and proteomics data related to Parkinson's disease patients, and carrying out quality control and standardization processing; through differential expression analysis and function enrichment, key genes and signal pathways related to Parkinson's disease are identified, and a potential drug target range is determined. By integrating genomics, transcriptomics and proteomics data of patients with Parkinson's disease, molecular mechanisms related to Parkinson's disease can be comprehensively analyzed, multi-target combination is optimized in combination with the ant colony algorithm, the limitation that a traditional single-target model is difficult to capture complex disease network comprehensiveness is effectively overcome, and the method is suitable for popularization and application. The accuracy of target spot prediction is remarkably improved, a more reliable action target spot is provided for drug research and development, and the failure rate of clinical tests is reduced.

Atherosclerotic plaque phenotyping by image data analysis, e.g., using conventional computed tomography angiography (CTA) or other imaging modalities can elucidate the molecular signature of atherosclerotic lesions on a per-patient basis. The present disclosure relates to methods of atherosclerotic plaque phenotyping to provide a subject-specific predictive model for gene expression referred to as virtual transcriptomics.

A spatial omics multi-modal fusion method under a single cell level comprises the following steps: extracting spatial morphological characteristics of differential expression genes and cell nucleuses from spatial transcriptome data, single cell sequencing data and histological images, and realizing field adaptation among different platforms by using a conditional variation auto-encoder. And based on a probability inference model, fusing spatial transcriptome expression, unicellular omics and morphological characteristics, and jointly inferring the type and gene expression level of each cell. A spatial cell network is constructed through a graph attention mechanism, and spatial diffusion and recognition of cell types in a full slice range are realized. In combination with a multi-omics enhancement module, undetected gene and protein expression is completed based on expression similarity, and prediction consistency is improved through spatial correction. According to the method, high-resolution reconstruction of single-cell multi-omics information in a three-dimensional space is realized, the information coverage and spatial resolution of spatial omics data are improved, and an efficient and low-cost solution is provided for spatial biology and precise medical research.

The invention is suitable for the technical field of tumor treatment and protection, and particularly relates to an intelligent classification prediction method, system and equipment for tumor treatment and protection, and the method comprises the steps: obtaining tumor-related molecular marker detection values and clinical diagnosis and treatment features of all target patients, and comprehensively integrating the multi-dimensional medical information of the patients; according to the relevance of molecular markers and clinical features, a scientific and quantitative multi-dimensional prognosis layering index set is constructed, and an accurate layering evaluation framework is formed; inputting the medical data into a pre-trained multilayer neural network model, and generating a high-resolution tumor characterization map through a cross-modal feature fusion technology; based on the dynamic matching of the atlas and the hierarchical index set, the hierarchical contribution degree parameter and the prognosis risk score are calculated in combination with the random forest model, the precise quantitative evaluation of the condition of the patient is completed, a high-credibility quantitative basis is provided for clinical decision making, the over-treatment risk is effectively reduced, and the prognosis management effect of the patient is improved.

The invention discloses a microbiological culture scheme customization recommendation method and system based on soil data analysis, and relates to the technical field of microbiological culture plants.The method comprises the steps that soil samples at different positions and depths are collected, soil characteristic data are analyzed, and data processing is carried out; microbial community information is obtained through soil DNA extraction, PCR amplification and high-throughput sequencing, and distribution and functions of microorganisms are evaluated in combination with data preprocessing and diversity analysis; selecting proper microbial strains according to soil characteristics and microbial community analysis results; a microbial culture scheme is combined with agricultural management measures; the conditions of soil and microorganisms are monitored in real time, and a microorganism culture scheme is dynamically adjusted based on a feedback mechanism; through field trials and advanced performance evaluation indexes, the effect of the microbial culture scheme is verified for a long time. The method has high adaptability and sustainability, and customized microorganism management schemes can be provided in different agricultural environments.

The invention provides a protein methylation site prediction method fusing quantum and region perception, which comprises the following steps: step 1, acquiring a protein sequence as a data source, and respectively constructing a training set and an independent test set; 2, constructing a multi-modal feature for each protein sequence by adopting a three-way nested scattering network, and fusing the multi-modal features to obtain an optimized fusion feature tensor; and step 3, inputting the optimized fusion feature tensor into a RaQMeNet network model, and performing a methylation site prediction task. The performance indexes of the method are greatly superior to those of the prior art, and the method has higher adaptability, stability and interpretability, can be widely applied to a plurality of bioinformatics and biological medicine related fields such as protein function annotation, disease mechanism research and drug target discovery, and has good application prospects and commercial values.

A system for recovering information lost during genomic data compression employs a quality-driven approach using neural networks. The system evaluates the importance of genomic regions through a quality analysis engine that assigns quality scores, while a rate control engine determines optimal compression rates based on these scores. A specialized neural network recovers lost information from correlated genomic datasets that have undergone lossy compression, utilizing recurrent layers for feature extraction and a channel-wise transformer with attention to capture complex relationships between data channels. The neural network architecture incorporates a deblocking network that combines these components to effectively reconstruct compressed data. A decoder receives and decompresses the data, then processes it through the neural network to recover information lost during compression. This adaptive system ensures critical genomic information is preserved while maximizing compression efficiency.

The invention discloses a transcriptomics spatial domain identification method, and belongs to the technical field of transcriptomics. The objective of the invention is to solve the problems of low data noise reduction precision and poor recognition effect of an existing spatial transcriptional spatial domain recognition method. The method comprises the following steps: firstly, obtaining an undirected neighborhood graph according to a gene expression matrix, obtaining embedded representation of the gene expression matrix by utilizing an encoder, obtaining a corresponding reconstruction matrix by utilizing a decoder, and further determining reconstruction loss; meanwhile, a ZINB model is used for fitting a reconstruction matrix, and a ZINB loss function is obtained; then, an augmented graph is constructed based on the undirected neighborhood graph, respective embedded matrixes are obtained through an encoder, the comparison loss of the undirected neighborhood graph and the comparison loss of the augmented graph are obtained through a comparison representation learning mechanism, and then the neighbor comparison loss is obtained; total target loss is obtained based on all losses, a joint optimization strategy is adopted for training, and after training of the whole model is completed, dimensionality reduction and spatial domain recognition are carried out on a generated reconstruction matrix.

The invention provides a protein expression prediction method based on a convolutional neural network and a residual attention mechanism, and belongs to the field of bioinformatics and artificial intelligence. The method comprises the following steps: performing data cleaning and binary one-hot coding on a DNA sequence, inputting the coded DNA sequence into a feature extraction layer, and extracting local features of DNA; local features are extracted through a convolutional neural network, global features are obtained through a multi-head attention mechanism, and residual connection is combined; and finally, inputting the global features into a full connection layer to generate a protein expression prediction result. Furthermore, an SHAP interpretability method is utilized to interpret the output result of the prediction model, and the contribution of the basic group at each position in the DNA sequence to the predicted value is quantified. According to the method, deep learning and interpretability technologies are combined, the precision and credibility of protein expression prediction are remarkably improved, and the method is suitable for predicting protein expression.

The invention discloses a multi-omics pathological analysis system and method for colorectal cancer liver metastasis risk prediction, and relates to the technical field of biomedicine and cancer diagnosis, the system comprises a data acquisition module, a data preprocessing module, a cell analysis module, a score construction module, a model construction and training module and a prediction and evaluation module; according to the method, multiple omics data including the single cell data set, the space transcriptome data, the batch RNA-seq data, the clinical data and the full slice image WSIs are integrated, comprehensive and accurate prediction of the colorectal cancer liver metastasis risk is achieved, cross-dimensional data fusion and analysis improve the prediction accuracy and reliability, and the risk of the colorectal cancer liver metastasis is reduced. The invention also provides a powerful tool for deeply exploring the molecular mechanism of tumorigenesis, development and metastasis, and particularly in a cell analysis module, cell subtypes and molecular characteristics closely related to liver metastasis can be disclosed by identifying and analyzing malignant cell subgroups LMTMCs triggering liver metastasis, so that the liver metastasis can be discovered. And a scientific basis is provided for formulating a personalized treatment scheme.

The invention discloses a three-dimensional space sequencing interpolation method and system based on a diffusion model, and the method comprises the steps: constructing a cell microenvironment diagram based on a spatial neighborhood relation, and building a correlation network of cell positions and gene expression; modeling continuous dynamic change of cell position and gene expression along with depth through a stochastic differential equation; learning a space gradient by using a graph neural network, and respectively predicting a shape drift coefficient and an expression drift coefficient; a bidirectional inference strategy is adopted to jointly optimize an interpolation result, and the alignment precision of shapes and expressions is constrained through a Wasserstein distance and a mean square error; and integrating the interpolation slices to generate a continuous three-dimensional transcription map. Through fusion of the diffusion model and the graph neural network, continuous complementation of z-axis slice data is realized, high-resolution reconstruction of tissue three-dimensional gene expression is realized, complex space biological problems such as tumor boundaries and brain region functional partitions can be accurately analyzed, and key technical support is provided for pathological diagnosis, development research and drug development.

Methods of deconvolving a feature profile of a physical system are provided herein. The present method may include: optimizing a regression between a) a feature profile of a first plurality of distinct components and b) a reference matrix of feature signatures for a second plurality of distinct components, wherein the feature profile is modeled as a linear combination of the reference matrix, and wherein the optimizing includes solving a set of regression coefficients of the regression, wherein the solution minimizes 1) a linear loss function and 2) an L2-norm penalty function; and estimating the fractional representation of one or more distinct components among the second plurality of distinct components present in the sample based on the set of regression coefficients. Systems and computer readable media for performing the subject methods are also provided.

The invention discloses an intestinal cancer metastasis prediction method and device based on spatial omics, a medium and equipment, and the method comprises the steps: collecting original multi-omics data, and carrying out modal alignment and quality control processing to obtain pre-processed multi-omics data comprising second spatial transcriptome data, second single-cell RNA sequencing data and second pathological image data; performing cross-modal semantic embedding on the second spatial transcriptome data based on the second single-cell RNA sequencing data to generate a spatial enhanced expression profile; performing multi-scale graph construction on the second spatial transcriptome data and the second pathological image data, and extracting spatial heterogeneity features; inputting the spatial enhancement expression spectrum and the spatial heterogeneity features into a pre-trained metastasis risk prediction model, and outputting a liver metastasis probability spatial heat map and a key driving feature list; and finally generating a clinical prediction report containing high-risk area positioning. According to the method, through dynamic optimization of spatial resolution and multi-scale feature collaborative modeling, the sensitivity of early transfer detection is remarkably 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.

Methods of deconvolving a feature profile of a physical system are provided herein. The present method may include: optimizing a regression between a) a feature profile of a first plurality of distinct components and b) a reference matrix of feature signatures for a second plurality of distinct components, wherein the feature profile is modeled as a linear combination of the reference matrix, and wherein the optimizing includes solving a set of regression coefficients of the regression, wherein the solution minimizes 1) a linear loss function and 2) an L2-norm penalty function; and estimating the fractional representation of one or more distinct components among the second plurality of distinct components present in the sample based on the set of regression coefficients. Systems and computer readable media for performing the subject methods are also provided.

The invention discloses a protein optimization design and screening method and device based on an artificial intelligence algorithm, and the method takes fungal luciferase as an example, and integrates multi-dimensional bioinformatics analysis and deep learning technology to realize efficient protein engineering transformation and optimization. The method comprises the following steps: firstly, identifying a binding domain of luciferase and fluorescein by utilizing an AI-driven molecular docking simulation method, and determining a key conservative site by combining literature, evolutionary analysis and structural prediction; then, generating a protein functional domain skeleton under the constraint of a fixed site by adopting a diffusion model, and performing protein sequence prediction by utilizing a graph neural network model; and finally, scoring and screening the generated sequences to obtain high-stability candidate variants. According to the method, a conservative site dynamic fusion strategy is innovatively constructed, a fixed region is optimized through logic of structure prediction, evolution site intersection priority and literature site union set expansion, the diversity and adaptability of a protein design sequence are met, and the efficiency bottleneck of a traditional scheme is broken through.

Platforms, systems, and methods for genetic generalization in synthetic biology development. According to one aspect, there is provided a method for predicting performance associated with genetic edits, the method comprising: receiving, by a platform, information about a strain of a microorganism, wherein the information about the strain comprises information describing a plurality of genetic edits to a base strain of the microorganism; generating, by the platform, a set of genetic embeddings based on the information about the strain, wherein the generating comprises processing the information about the strain using one or more embedding models, wherein each of the one or more embedding models: receives the information about the strain of the microorganism as input; and applies computational transformations to the input using a corresponding embedding model to generate a multi-dimensional vector representation for each of the plurality of genetic edits.

Methods for making synthetic gene clusters are described.

The invention discloses a rice nitrogen response regulation and control network analysis and breeding target identification system and method based on multi-omics data. According to the system, organic combination of regulation and control network construction based on single or multiple varieties of materials, key transcription factor recognition and accurate positioning of regulation and control areas where transcription factors play roles is achieved through an expression-chromatin accessibility correlation research method, and cis-trans effect distinguishing of the regulation and control areas is achieved through a deep learning model. The method comprises the following steps: carrying out nitrogen starvation pretreatment on rice, then carrying out nitrogen resupply, collecting a root sample, and carrying out ATAC-seq and RNA-seq sequencing; an eCAAS method is adopted to construct a regulation and control network, and key transcription factors are identified and accurately positioned; the chromatin accessibility difference of different varieties is predicted through a deep learning model, the cis-action effect and the trans-action effect are distinguished, an upstream transcription factor target is provided for genes dominated by the trans-effect, and haplotype and editable regulatory region targets available for direct breeding are provided for genes dominated by the cis-effect.

The invention relates to the technical field of marine organism protection, in particular to a marine organism diversity protection method based on an ecological dynamic model, which comprises the following steps: collecting biological and non-biological data in a marine ecosystem, extracting DNA from a seawater sample by utilizing an environmental DNA technology, and constructing the ecological dynamic model based on the collected ecological data and eDNA analysis result. According to the method, the ecological dynamics model is established, the collected data is stored and verified through the block chain technology, and a marine biological diversity protection scheme is designed and implemented according to the ecological dynamics model prediction result and the block chain verification data. Scientific support is provided for protection decision making, protection measures and management regulations are automatically executed through the intelligent contract technology, the system is allowed to respond immediately when it is detected that key environment variables or biological indexes change, and the flexibility and timeliness of responding to environment changes are greatly improved.

The present invention relates to a method for detecting a target analyte in a sample or a method for determining a quantification cycle (Cq) value for a target analyte in a sample. The present invention uses, in detecting a target analyte, an intersection point between a function representing an amplification curve for the target analyte and a first-order function obtained by connecting a first pole of an n-th order differential function or (n+1)-th order differential function for the function representing the amplification curve for the target analyte to a second pole of the (n+1)-th order differential function, thereby having excellent quantitative accuracy and precision in comparison to an existing method in spite of deviations between real-time PCR reaction experiments.

The invention relates to the technical field of bioinformatics, and provides a visual analysis method and system for a rice multi-tissue single cell expression profile. The method comprises the following steps: comparing sequencing data of an original single cell transcriptome of a rice tissue to obtain a standardized transcriptome data set; performing batch effect correction and integration on the standardized transcriptome data set to obtain a whole plant expression matrix; performing cell type annotation on the whole plant expression matrix to obtain a cell type annotation system; carrying out visual dimension reduction processing on the whole plant expression matrix fused with the cell type annotation system, and carrying out co-expression network construction to obtain a modular tissue correlation analysis model; and establishing an interaction end based on the module organization correlation analysis model, and realizing data visualization analysis through the interaction end. The invention provides a one-stop analysis platform for rice cell heterogeneity research, functional gene mining and molecular breeding.

The invention discloses a system and a method for predicting postoperative recurrence risk after triple negative breast cancer neoadjuvant therapy based on multi-modal time sequence medical image data, and belongs to the field of medical image analysis. The system comprises a data processing module used for constructing a multi-modal data set; the multi-modal feature extraction and screening module is used for extracting deep learning, radiomics and tumor habitat features from the region and carrying out feature screening; the model training module is used for constructing a time sequence model based on a Transform architecture and carrying out training through a multi-task learning strategy integrated with time consistency constraint and gene association auxiliary loss; and the recurrence risk prediction module is used for loading the trained model and outputting a recurrence probability and a risk level. According to the method, the multi-modal time sequence image and gene information are fused, so that the recurrence risk of the triple negative breast cancer patient is dynamically and accurately quantified, and support is provided for clinical individualized treatment decision.

The invention relates to the technical field of biological information, in particular to an immunoreaction evaluation method based on tumor neoantigen activity sorting. The method comprises the following steps: obtaining genome data of tumor and normal tissues through whole exon sequencing, extracting multi-dimensional features of candidate somatic mutation, and screening by combining a Gaussian mixture model and a Transform model to obtain a high-confidence mutation genome set; predicting the HLA genotype of a patient based on sequencing data, translating and mutating into a peptide fragment, predicting the binding affinity of the peptide fragment and an MHC molecule by using an XGBoost model, and calculating a new antigen activity score sequence in combination with various parameters; and finally, synthesizing a new antigen peptide fragment according to a sorting result, carrying out in-vitro co-culture to detect an IFN-gamma secretion result, and dynamically optimizing a characteristic combination coefficient through a PPO algorithm to realize intelligent iterative updating, so that the accuracy of new antigen screening and the immunoreaction prediction capability are remarkably improved.

The invention relates to a method for identifying a disease microorganism-metabolite combined marker based on a host-flora co-metabolism model. The method comprises the following steps: processing microbiome data; setting diet constraint conditions; determining classification composition of intestinal microorganisms of each individual, mapping abundance of each microorganism to a human microorganism genome scale metabolism model, and constructing an individualized flora metabolism network model; carrying out metabolic flow simulation on the constructed flora metabolism network model; integrating the constructed flora metabolism network model with the host metabolism network model, and predicting the metabolic flow and metabolite change of the whole system; and establishing a disease classifier based on a random forest machine learning algorithm to identify the microorganism-metabolite combined marker. According to the method, the complex metabolic process between the human body and the intestinal flora is restored in a metabolic network modeling and metabolic flow simulation mode, the method has the non-invasive characteristic, the time and economic cost is remarkably reduced, and meanwhile, the method has a great advantage in accuracy.

The invention provides a tumor detection data analysis method based on a peripheral blood immune marker, and belongs to the technical field of immunodetection.The tumor detection data analysis method comprises the steps that firstly, a peripheral blood sample of a patient is collected, and plasma and cell components are separated; analyzing various immune cell phenotypes by using flow cytometry; determining the concentration of key cell factors; analyzing the molecular expression level of the immune checkpoint; establishing an immune cell subset proportion empirical function; analyzing a cell factor expression mode by using an immune lineage transcriptome analysis model; establishing a tumor immune microenvironment score by combining the immune cell subset proportion and a model output result; the accuracy of the scoring system is verified through clinical follow-up visit and iconography examination; a standardized report template is formulated to provide a tumor early detection basis. Wherein the immune lineage transcriptome analytical model adopts a multi-modal attention fusion network architecture, three different immune data streams can be processed and integrated at the same time, and accurate evaluation of tumor-related immune states is realized.

The invention belongs to the field of bioinformatics, and relates to an anti-cancer drug response prediction method based on an attention mechanism. The method comprises the following steps: firstly, capturing uniform-dimension drug and cancer cell line characteristics through a multi-layer perceptron; secondly, fusing drug characteristics by adopting a Transform encoder, and constructing a cell encoder for cancer cell line characteristic polymerization; then, designing a cross-modal cross fusion module to promote information interaction between the two; and finally, predicting a semi-suppressed concentration value subjected to logarithmic transformation between the two through a multi-layer perceptron. Experimental results show that compared with an existing optimal method, the method has the advantage that the RMSE is reduced by 2.9%. According to the method, accurate prediction of the anti-cancer drug response is achieved by integrating drug and cancer cell line data, screening of potential anti-cancer drugs can be accelerated, personalized treatment schemes can be optimized, the cure rate of cancer patients is further increased, and the method has great significance in cancer treatment.

This application is directed to multiomic and biomimetic digital twin techniques for identifying disease-specific genetic variants. A computer system obtains information of subject genetic variants that are identified from a plurality of biological samples of a plurality of patients who are diagnosed with a target disease. A subset of subject genetic variants are selected based on a plurality of subject phenotypes of the target disease. The subset of subject genetic variants are ranked based on the plurality of subject phenotypes to generate subject genetic variant information. The computer system further obtains subject medical information of the plurality of patients. The computer system applies a biomimetic information model to process the subject genetic variant information, the subject medical information, and the general genetic variant information of the target disease and identify a set of target genetic variants associated with the phenotype of the disease(s) satisfying a variant selection criterion.