6379results 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 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 present disclosure relates to the field of biological information, and in particular to a gene regulatory network inference method based on spatiotemporal transcriptomic data, and the use thereof. In the present disclosure, on the basis of a graph neural network, a graph variational auto-encoder is used, an adjacency matrix A is used as node connection information, cells and genes are used as node feature information, and position information of the cells is fully used, so as to reconstruct a feature matrix of nodes by means of three hidden layers, thereby performing more accurate cell clustering and dimensionality reduction analysis while inferring a gene regulatory network.

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.

The invention relates to a method and a system for constructing a dynamic gene regulation network, computer equipment, a computer readable storage medium, a computer program product and a computer program. The method comprises the following steps: constructing a plurality of single cell quasi-time sequence differentiation trajectories based on single cell transcriptome data of a plurality of single cells; determining a dynamic change gene in a target quasi-timing sequence differentiation trajectory based on the plurality of single cell quasi-timing sequence differentiation trajectories; constructing an initial static gene interaction network based on the dynamic change gene; and performing inspection based on the initial static gene interaction network to obtain the dynamic gene regulation network. According to the method, key gene regulation mechanisms corresponding to different differentiation stages of the cells can be rapidly determined, and the real dynamic change condition of the gene regulation relationship in the biological process is reflected.

Methods and systems for performing in situ decoding are described that minimize optical crowding, thereby improving decoding accuracy. The methods may comprise, e.g., receiving images of a biological sample acquired during a cyclical decoding process; detecting a series of detectable signals (ON signals) or absence thereof (OFF signals) at one or more locations in the biological sample corresponding to one or more barcoded target analytes; determining a code word based on the series of ON and OFF signals that corresponds to a barcode for each of the one or more barcoded target analytes, where the one or more code words are assigned to the one or more barcoded target analytes based on a minimax decision rule to minimize a density of ON signals detected in the images of the series of images; and identifying the one or more barcoded target analytes based on the one or more determined code words.

Methods for making synthetic gene clusters are described.

The invention relates to the technical field of artificial intelligence, and particularly provides a KASP primer intelligent typing evaluation method and system TAL-SRX based on multi-model fusion. The method comprises the following steps: firstly, optimizing the performance of a basic model and a meta model by using RS, PSO and BO algorithms, constructing a Stacking integrated learning framework to integrate the advantages of an XGBoost learner, and enhancing the stability of the model by adopting five-fold cross validation; then, combining ANN and LSTM to construct a hybrid neural network, capturing a nonlinear relationship and extracting complex features, and meanwhile, introducing a Transform algorithm to capture a global dependency relationship in a high-dimensional feature space; and finally, fusing the two machine learning algorithms through a soft voting integration strategy, and outputting a KASP mark typing effect score. According to the method, the KASP marker with an excellent typing effect can be accurately screened, relatively high robustness and good and reliable evaluation performance are achieved, and technical support is provided for work such as molecular marker-assisted breeding.

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.

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.

The invention discloses a diagnostic marker of an acute myelogenous leukemia prognosis model as well as application and a prediction method of the diagnostic marker, and relates to the field of acute myelogenous leukemia prognosis research. The prognosis model is a prognosis model of acute myelogenous leukemia based on eight clinical characteristics, namely the disease age, the LDH level during disease, the RUNX1 gene, the KMT2A gene, the TP53 gene, the DMNT3A gene, the CBFB:: MYH11 gene and whether the first course of treatment is relieved or not; the construction method comprises the following steps of: sequentially carrying out univariate Cox regression analysis, Kaplan-Meier survival analysis and multivariate Cox regression analysis on clinical data of a patient from a data set and RNA sequencing data of peripheral blood and bone marrow samples in combination with survival information, further weighting an expression level according to an obtained regression coefficient, and carrying out quantitative analysis on the expression level. And establishing a prognosis model of acute myelogenous leukemia based on eight clinical characteristics. The acute myelogenous leukemia prognosis model disclosed by the invention has a relatively good evaluation effect.

The present disclosure provides new prime editor guide RNAs for prime editing, constructs for prime editing, and methods for using same. In addition, the present disclosure provides compositions and methods for conducting prime editing of a target DNA molecule (e.g., a genome) that enables the incorporation of a nucleotide change and / or targeted mutagenesis (e.g., insertion or deletion). The nucleotide change can include a single-nucleotide change (e.g., any transition or any transversion), an insertion of one or more nucleotides, or a deletion of one or more nucleotides. More in particular, the disclosure provides fusion proteins comprising nucleic acid programmable DNA binding proteins (napDNAbp) and a polymerase (e.g., reverse transcriptase), which is guided to a specific DNA sequence by a prime editor RNA (PEgRNA). The prime editor guide RNA comprises an extension arm that provides a DNA synthesis template sequence which encodes a single strand DNA flap, which is homologous to an endogenous DNA sequence, but which contains the desired one or more nucleotide changes and which, following synthesis by the polymerase (e.g., reverse transcriptase), becomes incorporated into the target DNA molecule.

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.

Provided are a protein language model training method, a method for extracting an amino acid sequence representation by using a protein language model, a method for obtaining a new amino acid sequence by using a protein language model, a method for obtaining a related amino acid sequence by using a protein language model, a method for predicting an amino acid sequence perplexity by using a protein language model, an electronic device, a computer-readable medium, and a program product. The protein language model training method comprises: during a training process, executing at least one training sub-task amongst two training sub-tasks included in a first type of training task and a second type of training task; and, on the basis of a first loss value corresponding to the first type of training task and a loss value corresponding to the second type of training task, adjusting parameters of a protein language model to obtain a trained protein language model.

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 relates to the field of bioinformatics and drug development, and discloses an efficient siRNA effectiveness prediction method and system based on an XGBOOST algorithm. The method comprises the following steps: acquiring a training data set containing siRNA sequence information, chemical modification information and corresponding validity data; carrying out cleaning, feature extraction and data balance processing on the data; training the prediction model by using an XGBOOST algorithm, and optimizing hyper-parameters; performing model evaluation and optimization through cross validation; a new siRNA sequence is input for effectiveness prediction; explaining a prediction result by using an SHAP value analysis technology; the actual interference efficiency of the efficient siRNA sequence is verified through an in-vitro cell experiment, and the model is further optimized based on an experiment result. According to the method, the accuracy and efficiency of siRNA effectiveness prediction are improved, the influence of chemical modification is comprehensively considered, the problem of data imbalance is solved, the model interpretation is enhanced, and the calculation efficiency is remarkably improved.

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.

The invention belongs to the technical field of gene sequencing, and particularly relates to an efficient and high-throughput gene sequencing data processing system. The data processing system comprises a data acquisition module, a data preprocessing module, a data storage and management module, a data comparison and alignment module, a variation detection module, a data integration and analysis module, a visualization and report module and a quality control and verification module, and the data acquisition module is used for extracting DNA or RNA and comprises phenol chloroform extraction, column purification and magnetic bead extraction; and detecting the concentration and the quality of the sample by using a spectrophotometer or a fluorescence quantifier, and carrying out pollution removal and degradation treatment on the extracted nucleic acid. According to the method, a comprehensive, accurate and efficient data processing solution can be provided for genomics research by optimizing the data processing flow, improving the data quality, enhancing the analysis capability and supporting automation.

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 present invention generally relates to systems and methods for imaging or determining nucleic acids, for instance, within cells. In some embodiments, the transcriptome of a cell may be determined. Certain embodiments are directed to determining nucleic acids, such as mRNA, within cells at relatively high resolutions. In some embodiments, a plurality of nucleic acid probes may be applied to a sample, and their binding within the sample determined, e.g., using fluorescence, to determine locations of the nucleic acid probes within the sample. In some embodiments, codewords may be based on the binding of the plurality of nucleic acid probes, and in some cases, the codewords may define an error-correcting code to reduce or prevent misidentification of the nucleic acids. In certain cases, a relatively large number of different targets may be identified using a relatively small number of labels, e.g., by using various combinatorial approaches.

The invention provides a method for hepatocellular carcinoma (HCC) subtype classification and treatment. The method can include identifying one or more features associated with a hepatic epithelial cell lineage for the hepatic epithelial cell lineage. The one or more features can be specified as a representation of a molecular subtype associated with hepatocellular carcinoma (HCC), such as, for example, a bile duct-like subtype, a hepatocyte-like subtype, or a progenitor cell-like subtype. If these characteristics are detected within a tumor sample of a patient, the patient can be determined as exhibiting the molecular subtype. In addition, the treatment of the patient can be determined based on the molecular subtype exhibited by the patient. For example, treatment of the patient can include additional therapies, such as GPC3 / CD3 bispecific antibodies, to overcome subtype-specific resistance to combinatorial immunotherapies associated with the progenitor cell-like subtypes. Related systems and computer program products are also provided.