1192results about "Systems biology" patented technology

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.

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.

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 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 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.

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 method for evaluating enhancement and release seedling resources based on environmental DNA polymerization analysis. The method comprises the following steps: carrying out gridding partition on a target water area, collecting a water sample through a designed sampling scheme, and carrying out DNA extraction and high-throughput sequencing to obtain species sequence information of each sampling point. Sequencing data is subjected to species identification by using a bioinformatics method, released species are identified, a spatial abundance model is established, and a preliminary distribution map is generated. And establishing a DNA degradation kinetic model in combination with water area environmental parameters, and carrying out reverse correction on abundance distribution. And through a resource inversion model coupled with hydrodynamics, analyzing biomass distribution characteristics and migration laws of the release group, and obtaining a resource evaluation result. And finally, a species environment preference model is constructed based on migration path analysis, an optimal release area is matched in a target water area, a scientific scheme including release point locations, opportunities and quantity is generated, and a whole-process technical support and a decision basis are provided for enhancement and release.

The invention discloses an intelligent marinating regulation and control method for leisure old marinated claw snacks based on multi-source sensing, which comprises the following steps: acquiring environmental parameters and material parameters in a marinating process through a multi-source sensor, and analyzing texture change of old marinated claws by utilizing a collagen triple-helix structure denaturation kinetic model; a dynamic time warping smell recognition algorithm is adopted to recognize a smell change stage, a color change rule is analyzed through a Maillard reaction kinetics coupling model, multiple parameters are fused to establish an incidence matrix, and the heating power of marinating equipment, the marinating liquid circulation rate and the ventilation quantity are regulated and controlled according to the matrix. According to the method, multi-parameter collaborative monitoring and deep analysis are realized, the regulation and control accuracy and the automation level are improved, the defects that a traditional method is insufficient in key component change analysis and poor in smell recognition and multi-parameter fusion collaboration are overcome, the product quality stability is guaranteed, and the standardized production requirement is met.

To provide a pharmaceutical composition for treating a cancer patient having a tumor having a total passenger gene mutation amount larger than the background mutation amount of the tumor.SOLUTION: A pharmaceutical composition for treating a subject having a tumor with a total passenger gene mutation load that is greater than the background mutation load of the tumor, wherein the background mutation load has been determined based on randomly selected genes of the tumor, comprising antibodies that bind to PD1 as an active ingredient. Antibodies that bind PD1 comprise a heavy chain variable region (HCVR) comprising the amino acid sequence of SEQ ID NO: 21 and / or comprise a light chain variable region (LCVR) comprising the amino acid sequence of SEQ ID NO: 22.SELECTED DRAWING: Figure 1

The invention discloses a farmland environment intelligent monitoring system and method based on multi-modal sensor fusion, and relates to the technical field of farmland environment monitoring, and the method comprises the steps: obtaining first integrated data in a farmland, and carrying out the preprocessing of the first integrated data; constructing a sound wave attenuation model based on the preprocessed first comprehensive data, and inverting the air humidity and the vegetation density through the sound wave attenuation model; conflict detection and resolution processing are carried out based on an inversion result; and constructing a three-dimensional microclimate field based on a conflict resolution result, and carrying out verification and iterative optimization by fusing a crop physiological model. According to the method, farmland space-time heterogeneity is adapted by means of a dynamic space-time weight coefficient, so that a three-dimensional microclimate field covering horizontal and vertical dimensions is constructed, and a'monitoring-verification-optimization 'closed loop is formed through crop physiological model association and iterative optimization; the problems of limitation of a single sensor, low inversion precision, poor data continuity, extensive monitoring and lack of decision association in the prior art are effectively broken through.

The invention discloses an administration agent in-vivo release and absorption visualization method based on a CFD-PBM coupling model. The method comprises the following steps: constructing a CFD model of a physiological environment of an injection site; establishing a population balance model (PBM) of the drug particles; the PBM is embedded into a CFD model solver, multi-scale coupling simulation is carried out, and a CFD-PBM coupling model is obtained; the three-dimensional visualization engine dynamically displays drug concentration distribution and particle behaviors; reversely adjusting preparation prescription parameters by using a multi-parameter optimization algorithm; and calibrating model parameters, and generating a key data report. According to the method, the prediction precision and the research and development efficiency can be remarkably improved, and the method is suitable for development of long-acting preparations such as microspheres and implants.

The invention discloses a molecular simulation method and device, a nonvolatile storage medium and electronic equipment. The method comprises the following steps: acquiring molecular system information of a current molecular configuration; calculating the first energy and the first gradient of the key atom region by adopting a variable component sub-feature solicitation solver algorithm, and calculating the second energy and the second gradient of the non-key atom region; wherein the first gradient is used for expressing the stress condition of each atom in the key atom area; the second gradient is used for expressing the stress condition of each atom in the non-key atom area; and updating the current molecular configuration according to the first energy, the first gradient, the second energy and the second gradient to obtain a target molecular configuration. According to the method and the device, the technical problem that the accuracy of the simulation result cannot be ensured under the condition that the computing resources are limited due to excessive computing resources consumed by the quantum chemical computing mode when the molecules are simulated by adopting the quantum chemical computing mode in the related technology is solved.

The invention discloses a polluted river water nitrogen and phosphorus removal efficiency evaluation system and method based on element analysis, and relates to the technical field of sewage treatment evaluation. An algae growth dynamic model is trained, and the future growth dynamic state of algae is obtained according to future water body data and future environment driving data; the method comprises the following steps: analyzing the interference influence of interference ion concentration on sensor measurement according to future water body data, dynamically simulating the attachment rate and biofilm thickness of algae on the surface of a sensor probe according to future growth of the algae, analyzing the attachment influence of the algae on sensor measurement, and correcting the measurement precision of the sensor according to the interference influence and the attachment influence. According to the corrected sensor data, whether the nitrogen and phosphorus removal efficiency of the polluted river water meets the removal standard or not is evaluated, real-time correction of the sensor data is achieved through interference ion concentration analysis and dynamic simulation of biological adhesion influence, and the accuracy of monitoring data and the scientificity of pollution treatment efficiency evaluation are effectively improved.

The invention provides a cell development process dynamic modeling method and device based on time sequence single cell transcriptome data and a medium, and relates to the crossing field of bioinformatics and computational biology. The method comprises the following steps: constructing a Shenchang differential equation learning framework; adjusting parameters of the single cell development state change model based on the Shenxuan differential equation learning framework so as to construct a population cell development state change model; obtaining a cell specific gene regulation network and a population cell gene regulation network based on the population cell development state change model so as to predict occurrence opportunity of cell lineage differentiation and a molecular decision mechanism of cell differentiation; therefore, the problems of incomplete modeling mechanism, insufficient noise processing and lack of energy principle in the existing cell development process are solved.

The invention provides a protein language model pre-training and protein mutation method and related products. According to one specific embodiment of the protein language model pre-training method, a sample protein data set is obtained; generating a multi-sequence alignment probability distribution sequence corresponding to the sample protein data according to the amino acid residue probability distribution of multiple sequences in each sample protein data alignment at each site; the sample protein sequence in each sample protein data and the corresponding multi-sequence comparison probability distribution sequence and structure sequence are sequentially connected in series in the forward direction or the reverse direction, and a multi-modal sequence corresponding to the corresponding sample protein data is generated; and finally, performing autoregression pre-training on the protein language model based on the multi-modal sequence corresponding to each sample protein data to obtain a pre-trained protein language model. Namely, the prediction performance of the model is improved by introducing a multi-sequence comparison probability distribution sequence as an independent intermediate reasoning mode and thinking chains in two directions.

The invention relates to the technical field of soil cadmium remediation, in particular to a soil Cd recognition and remediation method based on mulberry root exudates and microorganisms. According to the method, spatial microscopic image frames of rhizosphere microflora are divided through a community structure, community node increment updating calculation of adjacent time sequences is executed, the transition recombination frequency of the rhizosphere microflora is obtained, and the transition color of the actual concentration content of root exudates is rendered based on a metal pollution level system and the transition recombination frequency. Generating a stress level heat model of the soil cadmium gradient in the target restoration area; and according to the stress level popularity model, carrying out popularity color analysis and identification on the soil cadmium pollution of the target restoration area, and generating a global stress identification result of the soil cadmium. According to the method, rapid identification of cadmium pollution enrichment can be realized by utilizing the synergistic response between mulberry root exudates and rhizosphere microorganisms, and a repair regulation and control plan is made, so that the cadmium pollution degree is reduced, the soil structure and fertility are optimized, and a safer and healthier environment is provided for plant growth.

The invention discloses a flora prediction and petroleum pollution remediation method based on machine learning. The method comprises the following steps: collecting multiple groups of experimental data of a diesel oil pollution sample treated by a microbial agent, extracting environmental factors, microbial community characteristics and target response variables to construct a training data set after missing value processing, abnormal value detection and standardized pretreatment, and importing the data set into a preset machine learning model to obtain a training result; carrying out feature learning, classification training and hyper-parameter optimization by adopting a GridSearchCV method in combination with 10-fold cross validation; evaluating the correlation between a target response variable classification result and the features through a multivariable Pearson's correlation matrix, and constructing an optimal test set; and finally, selecting an optimal prediction model according to a preset index. According to the method, the model training quality is improved through data preprocessing and correlation analysis, efficient flora prediction and algorithm application evaluation are achieved by means of multiple machine learning algorithms, scientific support is provided for petroleum pollution remediation, and remediation accuracy and efficiency are improved.

The invention relates to the technical field of biological information, in particular to a brain glioma microenvironment formation key molecular mechanism analysis method, which comprises the following steps: calling expression data to analyze a cell marker sequence to construct a segmentation interval, calculating a candidate factor expression direction to judge trend consistency, identifying expression aggregation difference to construct a split node section, and constructing a subsection; according to the method, partitions are constructed on the basis of marker expression syn-position, judgment is carried out in combination with candidate factor expression directions and marker trends, factor collaboration features are constructed according to the number of trends consistent times, and the semantic sorting information is generated by analyzing the channel change trends to generate drift scores, evaluating multi-label output stability screening key factors and analyzing literature word order positions. Identifying and expressing an aggregation and split structure, guiding a functional pathway to perform trend analysis in a scoring interval and construct a dynamic trajectory, judging label stability and empowerment according to pathway scoring difference, and determining a factor semantic position in combination with a literature word order structure to realize integrated support of regulation and control information and literature evidence.

The invention discloses an alpha nuclide drug in-vitro cellular response prediction method and system based on multi-module mathematical modeling and a medium, and the method comprises the steps: constructing a basic parameter input module, a pharmacokinetic prediction module, a cell absorbed dose calculation module and a biological effect prediction module which are connected in sequence; a three-compartment dynamical model and a micro-dose learning point kernel convolution and DNA damage repair dynamical model are integrated, and full-chain and quantitative simulation from drug distribution, microscopic energy deposition to cell survival is achieved. The method overcomes the defects that an existing empirical model cannot accurately reflect the high LET characteristic of alpha particles, neglects the bystander effect and repair dynamics and the like, and the prediction precision is remarkably improved. According to the method, the curative effects of different drugs, cell lines and dosage schemes can be quickly simulated on a computer, the research and development cost and period are greatly reduced, and an efficient theoretical tool is provided for new drug screening and dosage optimization.

A haplotype genome assembly method and device, and related applications. The method comprises: acquiring short-read-length data and long-read-length data obtained by sequencing the same biological sample to be detected; performing error correction on the long-read-length data according to the short-read-length data to obtain error-corrected long-read-length data; performing genome assembly according to the error-corrected long-read-length data to obtain a preliminary assembly sequence; and optimizing the preliminary assembly sequence according to the short-read-length data to obtain a target assembly sequence. The method addresses the technical problem in the related art where obtaining high-quality genome assembly requires the use of three types of sequencing data, resulting in excessive data usage.

The invention discloses a diabetes cognitive impairment method based on metabonomics analysis and prediction, and relates to the technical field of biological information, and the method comprises the following steps: S1, obtaining metabonomics data and immunomics data from a peripheral blood sample of a diabetic patient, extracting relevant time sequence data aiming at glucose metabolism, and calculating the glucose metabolism related time sequence data; processing the sequence data by adopting a time sequence analysis algorithm to obtain time sequence change characteristics; s2, constructing a cross-omics interaction network according to time sequence change characteristics, integrating an incidence relation between metabolite concentration and immune factor expression, and setting a dynamic interaction mode; according to the diabetes cognitive impairment method based on metabonomics analysis and prediction, through multi-omics data integration and dynamic network analysis, the precision and reliability of diabetes cognitive impairment mechanism analysis are remarkably improved, and a theoretical basis is provided for precise intervention.

The invention provides a vaccine target screening system based on calculation model simulation. The vaccine target screening system comprises a multi-source heterogeneous database, wherein the multi-source heterogeneous database integrates and standardizes pathogenic genes, protein structures, literatures and experimental data; the feature calculation module calls a calculation biological model to carry out structural analysis, immunogenicity simulation and stability prediction; the intelligent screening and sorting module applies a multi-objective optimization algorithm to perform parallel evaluation and outputs optimal target spots; a structure iteration optimizer automatically iteratively corrects the optimized target spots to generate a high-potential variant library; and the process suitability simulation module couples the variants with the preparation formula and the process parameters to simulate production storage behaviors and feeds back an optimization target. According to the invention, efficient screening and optimization of vaccine targets can be realized, the accuracy and efficiency of target screening are improved, the research and development cost is reduced, and the research and development process of vaccines is accelerated.

According to one aspect, there is provided an AI-based platform which may include an experiment data set including records that respectively represent an experiment. Each record may indicate at least one hypothesis associated with the experiment and an experiment definition based on the at least one hypothesis. An AI-based agent may be configured to perform an evaluation of respective records of each experiment, and generate, based on the evaluation, at least one observation about the at least one hypothesis associated with the experiment represented by each of the respective records.

The invention relates to the crossing field of artificial intelligence technology and bioinformatics, and discloses a pig feed efficiency prediction model and system based on multi-omics data. Modulating a neural differential equation which runs on a priori knowledge graph and is realized by a graph neural network by using the matrix so as to solve and generate a continuous evolution trajectory of an individual physiological state; and finally, aggregating the tracks, combining the constraint matrix, and outputting a feed efficiency prediction value through a second preset model. The invention further provides a corresponding prediction system which comprises a static constraint module, a dynamic core module and a prediction module. According to the method, static genetic constraint and dynamic physiological process simulation are combined, genetic differences among different individuals can be reflected, and the biological consistency and individualization precision of a prediction model are improved.

The invention relates to the technical field of biomedicine, in particular to a liver cancer molecular subtype typing and prognosis model construction method based on glycometabolism and lactic acid metabolism related genes and application thereof. According to the invention, the typing and prognosis model of liver cancer subtypes is constructed for the first time on the basis of a coordinated regulation network of a glycometabolism gene and a lactic acid metabolism gene, the constructed liver cancer subtype subtypes comprise Cluster1 and Cluster2, and the total survival rates of different subtypes have significant difference; the clinical characteristics T.stage and TNM.stage of different subtypes are obviously different from each other; 12 kinds of immune cells have significant differences among different subtypes; iPS immune scores of different subtypes are significantly different; the TIDE scores of different subtypes have significant score differences; the genes with the highest mutation frequency in different subtypes comprise TP53, CTNNB1 and TTN. CLM scores of the prognosis model constructed on the basis of the collaborative regulation network of the glycometabolism genes and the lactic acid metabolism genes have no significant difference in different patient states; the 17 immune cells have significant differences between high and low risk groups. And a new way is provided for individualized treatment strategy formulation and survival outcome improvement of HCC.

Embodiments of the disclosure include systems and non-transitory computer readable media for analyzing microscopy images for developing machine learning models for disease modeling. Microscopy images are captured from cells of one or more exposure response phenotypes (ERPs) and further used to train machine learning models. Thus, trained machine learning models can distinguish between microscopy images captured from healthy and diseased samples.

The invention discloses a protein function prediction method based on multi-modal fusion and a dynamic label network, and belongs to the technical field of biological information, an adjacent matrix of a label association network can be smoothly updated in a model training process, and the protein function prediction method can be used for predicting protein functions by extracting various modal information of protein. The redundant relation among multiple modes is removed, the prediction effect of protein functions is improved, meanwhile, a training method combining a protein function association network and a tag association network is used, the influence of multiple tags on protein function prediction is considered, and protein function prediction is achieved by fusing the sequence, structure and structural domain information of protein. According to the method, the complementarity of various data is fully utilized, so that the prediction capability of the model is improved, and compared with a traditional method using a static label relationship, the scheme dynamically updates the label relationship in model training, so that the generalization capability of the model is further improved.

To provide a method for determining when a natural therapeutic or beneficial product will exert its therapeutic or beneficial effect via a physiological mechanism of action.SOLUTION: The present invention relates to a new method that allows the evolution of medical technologies from the use of chemically or biologically defined artificial substances to self-assembling natural products obtained from natural raw materials by industrial processes that preserve their endogenous properties, thereby preserving their ability to interact with the networks of the living world (including humans), which natural products cannot be defined with classical qualitative and quantitative composition schemes. Thus, the present invention provides a new method for determining when a therapeutic or beneficial product exerts its therapeutic or beneficial effect via a physiological mechanism of action. This method provides the tools necessary for the skilled person to assess the mechanism of action of a therapeutic or beneficial product, which has become a relevant feature to assess with new developments in regulatory regimes for medical devices and nutraceuticals, for which there is no method available in the art.SELECTED DRAWING: None

The invention belongs to the technical field of biology, and discloses a non-natural pathway generation method and system based on a continuous biological template, and the method comprises the following steps: obtaining an enzymatic reaction data set, and preprocessing the enzymatic reaction data set; constructing a directed reaction diagram on the basis of the preprocessed enzymatic reaction data set, performing similar compound search on a target compound by utilizing the directed reaction diagram, and obtaining a continuous group conversion template of the similar compound in the natural enzymatic pathway by taking the searched similar compound as a starting point and combining a depth-first search mode; the continuous group conversion template is applied to a target compound in a molecular linear input specification form, and an inverse synthesis process of the target compound is simulated to generate a non-natural synthesis pathway based on the continuous group conversion template. The method not only enhances the biological feasibility and construction success rate of the approach, but also has universality, expandability and engineering potential, and provides a generalizable normal form for the field of synthetic biology.

A method of modelling system behaviour of a physical system, the method including, in one or more electronic processing devices obtaining quantified system data measured for the physical system, the quantified system data being at least partially indicative of the system behaviour for at least a time period, forming at least one population of model units, each model unit including model parameters and at least part of a model, the model parameters being at least partially based on the quantified system data, each model including one or more mathematical equations for modelling system behaviour, for each model unit calculating at least one solution trajectory for at least part of the at least one time period; determining a fitness value based at least in part on the at least one solution trajectory; and, selecting a combination of model units using the fitness values of each model unit, the combination of model units representing a collective model that models the system behaviour.