3229results about "Systems biology" patented technology

Genome-wide data is obtained, and data cleansing, data sparsity processing and bioinformatics feature extraction are performed on the obtained genome-wide data; model construction is performed based on the sparsity-processed genome-wide data and the bioinformatics features to obtain a preliminary hybrid model; model training, regularization, and interpretability enhancement are performed on the preliminary hybrid model to obtain a trained model weight and an interpretability analysis corresponding to the trained model weight; learning and uncertainty estimation are performed based on the trained model weight and to-be-predicted genome-wide data on the hybrid model to obtain an integrated prediction result and uncertainties corresponding to the integrated prediction result; and personalized medical advice and decision assistance are performed based on the integrated prediction result, the interpretability analysis, and the uncertainties corresponding to the integrated prediction result.

A federated distributed computational system enables secure oncological therapy optimization through robotic integration. The system establishes a distributed graph architecture with secure communication channels connecting computational nodes, implementing encryption protocols for cross-institutional data exchange. Each node contains processing capabilities for fluorescence-guided imaging, uncertainty quantification, and expert knowledge integration while maintaining hierarchical knowledge graphs of oncological biomarkers, interventions, and outcomes. The system coordinates domain-specific knowledge through token-space communication and implements an advanced robotic integration system for surgical interventions using spatiotemporal tumor mapping, multi-modal fluorescence imaging, surgical robot coordination, and space-time stabilized mesh management. Key capabilities include wavelength-specific multi-modal fluorescence detection, combined epistemic and aleatoric uncertainty estimation, tensor-based data integration with adaptive dimensionality control, and light cone search for adaptive treatment optimization—all while maintaining strict privacy controls.

The AI-enhanced cellular modeling and simulation platform is a computational system designed to enhance biomedical research and development and personalized medicine and wellness. This platform integrates simulation modeling, machine learning and artificial intelligence, multi-omics data, and sophisticated data fusion and decision-support techniques to create comprehensive models of cellular systems and processes across multiple scales. It enables researchers and clinicians to simulate complex biological interactions, predict disease progression, and design or optimize treatment strategies or medical devices with improved accuracy and efficacy. The system's architecture allows for integration of various components, including real-time data processing, federated learning, and quantum computing enhancements. From personalized drug discovery and cancer therapies to synthetic biology and epidemiological analysis, this platform offers powerful tools for understanding and manipulating cellular systems and bioengineered systems. By bridging the gap between molecular-level interactions between cells and materials and organism-wide effects, it enables significant advancements in healthcare and biological sciences.

A federated distributed computational system enables secure oncological therapy optimization through multi-expert integration and advanced uncertainty quantification. The system implements a multi-expert integration framework that coordinates domain-specific knowledge through token-space communication for precision oncological treatment, while maintaining secure cross-institutional data exchange. The architecture coordinates multi-scale spatiotemporal synchronization across computational nodes, with each node containing local processing capabilities for fluorescence-guided imaging, uncertainty quantification, and expert knowledge integration. Through a distributed graph architecture, the system enables advanced fluorescence imaging with wavelength-specific targeting, multi-level uncertainty estimation combining epistemic and aleatoric approaches, and multi-scale tensor-based integration with adaptive dimensionality control. The system implements light cone search and planning for adaptive treatment strategy optimization, enabling medical institutions and research organizations to collaborate on complex oncological therapy projects while maintaining strict data privacy controls.

A federated distributed computational system enables secure, multi-institutional biological data analysis and genomic medicine through interconnected, decentralized nodes in a federated distributed graph architecture. A federation manager coordinates computational resource allocation, control and data flows, establishes privacy and security boundaries, implements multi-scale spatiotemporal analysis and simulation modeling, models cross-species or intrapopulation elements, and maintains cross-institutional knowledge relationships. Each node includes a local processing unit for biological data analysis, including multiomics and gene editing, privacy-preserving protocols for secure multi-party computation, a hierarchical knowledge graph for managing multi-domain biological relationships across spatial and temporal scales, and encrypted network connections. The system implements cross-species genetic analysis via phylogenetic integration, environmental response modeling through spatiotemporal tracking, and multi-scale tensor-based data integration with adaptive dimensionality control. This architecture enables research institutions to collaborate on complex biological analyses and genomic medicine applications while maintaining strict data privacy and security controls.

A federated distributed computational system enables secure biological data analysis and genomic medicine through hybrid simulation capabilities. The system implements a hybrid simulation orchestrator that coordinates classical numerical simulations with machine learning models for biological system analysis, while maintaining secure cross-institutional data exchange. The architecture coordinates multi-scale spatiotemporal synchronization across computational nodes, with each node containing local processing capabilities for biological data analysis and privacy preservation protocols. The system implements cellular machinery assembly analysis, real-time patient data integration, and multi-modal image integration with spatiotemporal health data annotation. Through a distributed graph architecture, the system enables cross-species genetic analysis, environmental response modeling, and multi-scale tensor-based data integration with adaptive dimensionality control. The system implements real-time therapeutic response prediction through multi-modal data analysis, enabling research institutions to collaborate on complex biological analyses while maintaining strict data privacy controls.

The invention relates to the technical field of biological information, in particular to a stem cell culture information management method and system. The method comprises the following steps: collecting culture process basic data in a stem cell culture process, wherein the culture process basic data comprises cell counting data, culture condition parameter data and pollution condition data; performing growth kinetic analysis on the basic data in the culture process to obtain cell growth characteristic data; establishing a cell growth prediction model based on the cell growth characteristic data, and generating cell growth early warning data; and monitoring the culture process in real time according to the cell growth early warning data, triggering an early warning signal when abnormal fluctuation is detected, and generating a culture abnormality processing scheme. The growth prediction model is established based on the cell growth characteristic data, the culture dynamic state can be monitored in real time, an early warning signal is provided, the detection and response speed of abnormal fluctuation is remarkably improved, and culture failure is avoided.

The invention relates to the technical field of breeding planning, in particular to an intelligent breeding planning and decision-making method and system based on a large model. The method comprises the following steps: acquiring a multi-source breeding data set; constructing a structured breeding knowledge graph based on the multi-source breeding data set; performing breeding data association on the structured breeding knowledge graph according to a preset large model to generate a special breeding basic model; obtaining a breeding instruction input by a user; performing user semantic recognition on a breeding instruction input by a user to generate breeding semantic recognition data; inputting the breeding semantic recognition data into a breeding special basic model for breeding intention analysis, and generating user breeding intention data; and determining data information needing to be called based on the breeding intention data of the user, analyzing and screening to generate germplasm resource screening data and a breeding plan / breeding decision scheme. According to the method, the intelligence and operability of breeding planning are improved through integration of multi-source data, intelligent semantic recognition, combined genetic analysis and executable evaluation.

The invention provides a virtual-real fusion method for digital twinning operation of a cell drug factory in combination with the Internet of Things, which comprises the following steps: constructing geometric models of a culture medium storage tank and a bioreactor through three-dimensional modeling software, mapping and associating historical process parameters of a culture medium supply system and temperature, pH value and dissolved oxygen concentration monitored in real time with the geometric models, and obtaining a virtual-real fusion model of the culture medium storage tank and the bioreactor; constructing a digital twinborn model of the reactor; analyzing historical production data through a machine learning algorithm, extracting a quantitative relationship among key parameters of a cell metabolism rate, a glucose concentration in a culture medium, a pH value and a reactor temperature, establishing a cell growth prediction model, and predicting a growth trend and a metabolism demand of cells in a target time period; a virtual-real mapping mechanism of a culture medium supply system is integrated with an intelligent manufacturing execution system, so that the culture medium supply process and the whole pharmaceutical production process are subjected to collaborative optimization control, and the working procedures of the culture medium production process are automatically dispatched according to a production plan and cell growth requirements.

An apparatus for training a tunable data structure to predict internal ribosome entry site (IRES) activity includes at least a processor and a memory containing instructions configuring the at least a processor to assemble a training set including a plurality of nucleotide sequence data examples IRES sequences and a plurality of correlated observed IRES activity, partition the training set into at least a first section and a second section, train, using the first section at least an activity data structure to generate probable IRES activity using nucleotide sequence data, and iteratively retrain the at least an activity data structure using the second section, wherein each iteration of the iterative retraining includes generating a predicted IRES activity value using the at least an activity neural network and a nucleotide sequence data example, evaluating an error function, and tuning the activity data structure.

A method and system for determining one or more sources of a cell free deoxyribonucleic acid (cfDNA) test sample from a test subject. The cfDNA test sample contains a plurality of deoxyribonucleic acid (DNA) molecules with numerous CpG sites that may be methylated or unmethylated. A trained deconvolution model comprises a plurality of methylation parameters, including a methylation level at each CpG site for each source, and a function relating a sample vector as input and a source of origin prediction as output. The method generates a test sample vector comprising a site methylation metric relating to DNA molecules from the test sample that are methylated at that CpG site. The method inputs the test sample vector into the trained deconvolution model to generate a source of origin prediction indicating a predicted DNA molecule contribution of each source.

The invention discloses a microbial fermentation strain breeding control system based on a neural network model, and relates to the technical field of digital fermentation, and the system comprises a thallus data acquisition module, a biological feature recognition module, a prediction classification module, a breeding decision module, an optimization training module, an early warning monitoring module and a sorting regulation module. The bacterial data acquisition module preprocesses bacterial data; the biological feature recognition module constructs a bacterial feature library; the prediction classification module predicts the adaptability of bacterial strains and classifies the bacterial strains; the selection decision module selects the bacterial strains and generates culture parameters; the sorting regulation and control module generates a three-dimensional scatter diagram and a hereditary character report. According to the method, the precision and efficiency of breeding of the microbial fermentation strain are remarkably improved, intelligence and automation of the breeding process are achieved, the labor cost is reduced, the stability and controllability of the fermentation process are enhanced, and powerful technical support is provided for innovative development of the microbial fermentation industry.

The invention relates to the technical field of data processing. The fermentation process intelligent regulation and control method and system based on multi-modal perception are provided, and the method comprises the following steps: carrying out image feature extraction processing on microorganism image data to generate a morphological feature vector, and carrying out metabolic feature dimension reduction processing on metabonomics data to generate a metabolic feature matrix; performing time sequence alignment processing to generate a fusion feature matrix, and performing abnormal marking processing on the metabonomics data to generate abnormal marking data; carrying out correlation intensity calculation processing on the morphological change of the microorganisms and the concentration fluctuation of the metabolites to generate a dynamic correlation intensity curve; constructing a cross-dimensional anomaly recognition model and a multi-modal collaborative prediction model, and generating a regulation and control parameter suggested value; the parameters of the multi-modal collaborative prediction model are updated through a feedback learning mechanism, the feature fusion weight of the fusion feature matrix is optimized, the accuracy of anomaly detection and regulation decision is improved, and the risk of stability fluctuation in the fermentation process is reduced.

The invention provides a method and a system for screening and constructing a small molecule polypeptide simulant based on interaction between proteins, relates to the technical field of biology, and comprises the following steps: predicting an interaction model of RECK protein and ADAM10 protein through a molecular docking platform, and analyzing interaction sites of the RECK protein and the ADAM10 protein. And then, determining a KAZAL region of the RECK protein according to the interaction site, and predicting a sequence structure of the KAZAL region. And then carrying out molecular docking on the KAZAL region and ADAM10 protein, designing a plurality of peptide fragments with the length of 30 amino acids, and screening the peptide fragment sequence with the highest score as candidate polypeptide. And finally, performing species homology comparison and quality control detection on the candidate polypeptide, and verifying the binding capacity and biological function of the candidate polypeptide to ADAM10 protein through cell experiments. The invention provides a method for screening to obtain a polypeptide simulant specifically bound with ADAM10 protein. The polypeptide simulant can be used for treating ADAM10 related diseases.

The invention relates to the technical field of crop saline-alkaline tolerance analysis, in particular to a rice saline-alkaline tolerance prediction method and system based on big data analysis, and the method comprises the following steps: collecting soil environment data, rice physiological phenotype data and historical stress response data, and constructing a multi-dimensional data cube by using a space-time alignment engine; quantization feature analysis is adopted to carry out quantum probability modeling on Na < + > / K < + > ion transmembrane transport and PSII photosynthetic exciton transfer features, and a quantum probability cloud chart of rice physiological response is generated. Based on a geodesic line domain adaptation model of geodesic line mapping, a soil conductivity thermodynamic diagram and a quantum probability cloud chart are fused, a rice saline-alkaline tolerance adaptability prediction model is established, finally, a three-dimensional saline-alkaline tolerance performance prediction map is output, and the adaptability of rice varieties in specific saline-alkaline land is visually displayed. According to the method, the regional generalization ability of saline-alkali tolerant varieties is effectively improved, the limitation of a traditional statistical model is broken through, and a scientific basis is provided for saline-alkali tolerant rice breeding and cultivation and saline-alkali soil agricultural optimization management.

Detecting cross-contamination between test samples used for determining cancer in a subject is beneficial. To detect cross-contamination, test sequences including at least one single nucleotide polymorphism are prepared using genome sequencing techniques. Some of the test sequences can be filtered to improve accuracy and precision. A prior contamination probability for each test sequence is determined based on a minor allele frequency. A contamination model including a likelihood test is applied to a test sequence. The likelihood test obtains a current contamination probability representing the likelihood that the test sample is contaminated. The contamination model can also determine a likelihood that the sample includes loss of heterozygosity representing the likelihood that the test sequence is contaminated. Test samples that are contaminated are removed. A source for the contaminated test sample can be found by comparing contaminated test sequences to other test sequences.

Provided is a method for calculating a carbon stock of a mixed forest ecosystem, which method belongs to the technical field of carbon sink estimation of forest ecosystems. The method comprises: acquiring basic geographic data, meteorological data, physiological and ecological parameters, historical operation data and verification data; on the basis of an existing Biome-BGC model, improving a phenological module, adding a thinning operation management module, and optimizing the physiological and ecological parameters, so as to propose an improved Biome-BGC model suitable for simulating a carbon stock of a managed mixed forest ecosystem; taking a mixed pine and oak forest as a research object, and using the improved model to simulate a carbon stock; verifying the improved model; using an extended Fourier amplitude sensitivity test method to analyze the sensitivity of the physiological and ecological parameters; and on the basis of selecting high-sensitivity parameters, using a path analysis method to analyze the influence of the high-sensitivity parameters on the carbon stock. The proposed improved model exhibits a better performance in the aspect of a carbon stock of a mixed forest.

The invention provides a tumor immunotherapy curative effect prediction system based on polarization characteristics of neutrophil, and relates to the technical field of biology, the system comprises a neutrophil detection module used for determining neutrophil information; the patient information module is used for acquiring tumor information and immune information of a patient; the immunotherapy scheme information module is used for acquiring immunotherapy scheme information of the patient; the historical data acquisition module is used for acquiring historical patient information; the immune state coefficient module is used for determining the immune state coefficient of the patient; the prediction model module is used for obtaining the predicted tumor size through the trained tumor size prediction model; the immune state prediction module is used for determining a predicted immune state coefficient; and the curative effect prediction module is used for determining a predicted tumor immunotherapy curative effect score. According to the invention, the predicted tumor immunotherapy curative effect can be evaluated according to two aspects of predicting the tumor size and predicting the immune state coefficient.

The invention relates to a parallel processing synchronous optimization method and system for high-throughput gene sequencing. The method comprises the following steps: performing channel distribution and signal acquisition initialization on a plurality of DNA samples to obtain a signal acquisition parameter matrix; performing feature extraction and clustering analysis on the fluorescence signals collected by each channel to obtain a DNA base feature sequence and a signal classification probability matrix; dynamically adjusting short-term parameters of a sequencing period through a convex optimization algorithm, and optimizing long-term sequencing performance through an integer programming algorithm to obtain a sequencing parameter optimization set and a sequencing period distribution strategy; and inputting the sequencing parameter optimization set and the sequencing period allocation strategy into a double-layer nested control model, and carrying out joint solution through an upper-layer resource allocation network and a lower-layer parameter optimization network to obtain a global optimization control parameter set. According to the method and the device, the sequencing accuracy is improved, and the utilization rate of computing resources is improved.

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.

The invention belongs to the technical field of deep learning, and discloses a drug and protein interaction prediction model construction method based on multi-modal fusion. Due to the lack of protein three-dimensional data, the three-dimensional data of the protein is subjected to high-accuracy complementation by using a prediction model AlphaFold 2, and for the modal fusion problem, the drug molecules are fused at the atomic level with finer granularity, so that the multi-modal fusion effect is further enhanced, and the multi-modal fusion effect is improved. Protein 1D data and protein 3D data are processed by using Cross-Attention, the processed protein 1D data and protein 3D data are subsequently fused by using Cross-Attention, multi-modal fusion is performed by using Cross-Attention, so that the interaction relationship between modals can be carefully captured, and in a word, the DTI task effect is improved by designing a multi-modal fusion mode.

The invention provides a method for obtaining a cell survival score model based on different depths, a terminal and a medium, and the method comprises the steps: determining neutron flow beams corresponding to each target depth, and carrying out a cell survival experiment based on a cell survival experiment device through employing the neutron flow beams corresponding to the target depths for different target depths, thereby obtaining the cell survival score model based on the cell survival score model. Obtaining a plurality of groups of fitting sample pairs under the target depth; and fitting model parameters in a cell survival score initial model by using the fitting sample pair under each target depth, so as to obtain a cell survival score model corresponding to each target depth, according to the method, the cell survival score models suitable for different target depths can be rapidly obtained, the obtaining efficiency of the models of different target depths is improved, the models are solved by utilizing multiple groups of fitting sample pairs, and the model accuracy of the cell survival score models is also effectively improved.

The present disclosure provides systems and methods for machine learning classification and assessment of COVID-19 disease based on gene expression data, including prediction of disease severity. In an aspect, a method for determining a COVID-19 disease state of a subject may comprise: (a) assaying a biological sample obtained or derived from the subject to produce a data set comprising gene expression measurements of the biological sample of each of a plurality of COVID-19 disease-associated genes; (b) computer processing the data set to determine the COVID-19 disease state of the subject; and (c) electronically outputting a report indicative of the COVID-19 disease state of the subject.

The invention discloses a distributed farm bioaerosol concentration simulation and health risk early warning method based on deep learning, and the method comprises the following steps: constructing and deploying a distributed sensor network, collecting and processing data, constructing a bioaerosol concentration distributed model and a health risk early warning model based on a deep learning method, and through a central processing control and 3D visualization unit, the simulation and prediction result of the bioaerosol concentration distributed model and the health risk level and early warning information generated by the health risk early warning model are used to generate a visual 3D graph. The risk of pathogen transmission is greatly reduced, the safety level of the farm environment is remarkably improved, and an intelligent and systematic solution is provided for guaranteeing the health of workers and animals.

The invention discloses a colorectal cancer drug relocation method based on multi-omics integration. The system comprises a multi-omics data acquisition and preprocessing module, a tumor microenvironment analysis module, a specific disease network construction module, a multi-dimensional drug relocation module and a result evaluation module. And the tumor microenvironment analysis module comprises cell heterogeneity identification, cell map construction, cell annotation and tumor cell subset annotation. The specific disease network construction module comprises tumor feature expression program extraction, expression program screening, meta-program construction, clinical related meta-program recognition and specific disease protein interaction network construction. And the multi-dimensional drug relocation module comprises a module for identifying diseases by using a random walk algorithm, carrying out drug screening based on disturbance data, carrying out drug screening based on network proximity and carrying out comprehensive drug relocation. From the perspective of single cell data, element programs related to colorectal cancer survival are excavated, corresponding modules are designed, and the efficiency and precision of colorectal cancer targeted drug screening are improved.

The invention discloses a global function domain-introduced Cas protein classification method and system. The method comprises the following steps of: reading a Cas protein sequence file, preprocessing the Cas protein sequence file and constructing positive and negative sample pairs; a LoRA dynamic rank adjustment mechanism based on Cas protein sequence global functional domain priori is introduced on a pre-trained protein language model, the trained protein language model is used for Cas protein sequence classification, functional domain coverage frequency of each position in a Cas protein sequence is used for quantizing functional importance of each position to generate a global hotspot functional domain vector, and the global hotspot functional domain vector is used for classifying the global hotspot functional domain. And dynamically guiding different LoRA layer rank parameters in the LoRA model, and adjusting weight parameters of the protein language model. According to the technical scheme, on the basis of a prior LoRA dynamic rank adjustment mechanism of a global functional domain of a Cas protein sequence and through a layered dynamic strategy, rank distribution is highly consistent with functional domain evolution conservative property and structural characteristics, and the model is endowed with higher biological interpretability.

The invention belongs to the field of bioinformatics, and relates to an improved integrated deep learning cell communication ligand-receptor interaction prediction method. The method comprises the following steps: firstly, carrying out extraction and dimensionality reduction on biological sequence features of a ligand and a receptor, and constructing multi-modal feature input; secondly, constructing an improved deep neural network branch, introducing a batch normalization layer and a Leaky ReLU activation function, solving the problems of gradient disappearance and neuronal necrosis, and improving regularization strength to prevent overfitting; meanwhile, an enhanced heterogeneous graph auto-encoder branch is constructed, the graph embedding dimension is remarkably expanded to improve the feature capacity, and full convergence of the model is ensured by increasing training rounds; thirdly, fusing the improved deep network with the prediction probability of a heterogeneous graph auto-encoder by adopting a weighted integration strategy; and finally, outputting a potential interaction relationship based on the fusion probability. By optimizing the architecture and the strategy, the prediction accuracy and robustness are remarkably improved, and a reliable tool is provided for analyzing a complex cell communication network.

The invention discloses a method for male biological age assessment and aging prediction based on DNA methylation data of peripheral blood. The method comprises the following steps: screening out DNA methylation sites related to male biological age and senescence by retrieving related literatures and research results; downloading DNA methylation chip sequencing data, and screening out male data; screening the modeling database and the DNA methylation sites to obtain a modeling sample and modeling sites; establishing an epigenetic clock model; comparing the epigenetic clock model with a traditional model, and performing model evaluation; carrying out biological age evaluation and aging prediction; according to the method, the regression models are established according to three methods based on screening of global male samples, few loci with top correlation ranking are selected from the regression models, few locus models are re-established to evaluate the age and the aging degree of people, and the method has the advantages of accurate male sample positioning, low prediction cost and convenience in later product popularization.

A federated distributed computational system enables secure drug discovery and resistance tracking through hybrid simulation capabilities. The system implements a hybrid simulation orchestrator that coordinates molecular dynamics simulations with machine learning models for drug discovery analysis, while maintaining secure cross-institutional data exchange. The architecture coordinates multi-scale spatiotemporal synchronization across computational nodes, with each node containing local processing capabilities for molecular dynamics simulation and resistance pattern detection. Through a distributed graph architecture, the system enables real-world clinical data integration, resistance evolution tracking, and multi-scale tensor-based analysis with adaptive dimensionality control. The system implements real-time drug response prediction through multi-modal data analysis, enabling pharmaceutical companies and research institutions to collaborate on complex drug discovery projects while maintaining strict data privacy controls.

The invention discloses a deep learning prediction method for drug-protein interaction, and the method comprises the steps: S1, collecting drug and protein data, and calculating the similarity of drugs and the similarity of proteins; s2, extracting feature vectors of proteins and drugs in each batch; s3, calculating the contrast loss in each batch based on the similarity of the drugs, the similarity of the proteins and the feature vectors of the proteins and the drugs in each batch; s4, splicing the feature vectors of the proteins and the drugs in each batch, and calculating the cross entropy loss in each batch according to the splicing result to obtain the final loss; and S5, performing model training according to the final loss, and inputting protein codes and drug codes into a trained model to obtain a prediction result. The high-dimensional information is introduced into the features while direct modeling of sparse high-dimensional data is avoided, and the accuracy of drug-protein interaction prediction is improved.