176results about "Systems biology" patented technology

Provided are a method for constructing a disease risk alert model, a method and apparatus for alerting to a disease risk, and an electronic device, a computer-readable storage medium, a computer program product and a computer program. The method comprises: acquiring a first input feature on the basis of genomic data of cell-free DNA in a sample; on the basis of a disease phenotype of the sample, acquiring a second input feature; and on the basis of the first input feature and the second input feature, constructing a disease risk alert model, wherein the genomic data comprises a fragment length of the cell-free DNA, sequence information of the cell-free DNA and position information of the cell-free DNA aligned to a reference genome.

This invention belongs to the field of bioinformatics and relates to a single-cell trajectory inference method based on adaptive feature selection. First, an initial gene expression matrix is ​​obtained through data preprocessing and screening for highly variable genes. Second, a two-dimensional evaluation strategy is employed to calculate the scores of highly variable genes with gene expression variability and the trajectory importance score related to differentiation trajectories. Then, a dynamic weight fusion mechanism is introduced, adaptively adjusting the fusion weights of the two scores based on performance feedback, and highlighting key genes through nonlinear enhancement. Next, an intelligent inflection point detection algorithm adaptively determines the optimal number of features. Finally, trajectory inference is performed based on a variational autoencoder model reconstructed from feature subsets, and a performance-driven feature selection closed loop is formed through multiple rounds of iterative optimization. This invention achieves high-precision, adaptive single-cell trajectory inference, solving the technical problems of single feature selection and fixed weights in traditional methods.

Multi-scale footprinting of DNA-protein interactions is described. Multi-scale footprint scores may be generated based on chromatin accessibility data, the multi-scale footprint scores indicating protein binding to positions of a genome at different protein size scales. A deep learning model may be trained using the multi-scale footprint scores and corresponding DNA sequences. DNA-protein interactions for a DNA sequence of interest may be predicted using the trained deep learning model. The prediction may include generating sequence attribution scores for the DNA sequence of interest using the trained deep learning model and predicting transcription factor binding sites of the DNA sequence of interest based on the sequence attribution scores.

Provided are a method and system for predicting an activation potency of agonist molecules on G Protein-Coupled Receptors (GPCRs). The method includes: blindly speculating complex structures formed by binding of a ligand to an activated receptor structure and an inactivated receptor structure, respectively, via global molecular docking; extracting an initial path enabling an inactivated complex structure to be activated to an activated complex structure based on an enhanced sampling algorithm; searching for a minimum free energy path closest to the initial path by applying an automatic path optimization algorithm; calculating a free energy distribution curve along the minimum free energy path by employing umbrella sampling and determining an energy barrier height and a free energy difference before and after activation, thereby determining the activation potency of the ligand structure on the GPCRs.

Time-series data formed by acquiring values indicating amounts or states of a plurality of biological materials for each biological material at a plurality of time points is prepared, the plurality of biological materials are divided into a plurality of groups on the basis of temporal variations of time-series data of the respective biological materials, representative time-series data indicating a state of each group is generated on the basis of time-series data of at least one biological material included in each group, and dependencies between the groups are estimated on the basis of the representative time-series data of each group.

A method for maintaining a gene description dataset associated with a plurality of genes with respect to a given disease, wherein the gene description dataset is a human- comprehensible text-based dataset generated by a Large Language Model (LLM); maintaining a dataset of drug mechanisms of action (MOAs) associated with a plurality of drugs; embedding the gene descriptions and the MOAs into high-dimensional vectors using a text embedding technique to generate gene description vectors and MOA vectors; calculating distances between the gene description vectors and the MOA vectors to generate distance scores; and ranking the drugs for the given disease based on an aggregated distance score between the MOA vectors and the vectors of genes associated with the given disease.

The application discloses a kind of based on carbon-based modified electrode microbial quantity dynamic evaluation method.The method comprises the following steps: preparing carbon-based modified electrode doped with target microorganism;Carbon-based modified electrode is used as working electrode, and reference electrode and counter electrode constitute three-electrode system, and is placed in the medium containing target microorganism to be measured in the electrochemical scanning of target microorganism, and the electrochemical response signal is collected;The electrochemical response signal is processed, and the characteristic electrochemical parameter of target microorganism metabolite is obtained;According to the preset detection time threshold selection model: when detection time is less than or equal to preset duration, linear model is used;When detection time is greater than preset duration, dynamic metabolism model is used;Characteristic electrochemical parameter is substituted into the selected model, and the number of microorganisms in the medium to be measured is calculated.The application can realize accurate quantification, anti-interference detection and rapid dynamic evaluation of the number of microorganisms, and significantly improve detection efficiency and accuracy.

The application provides a mass spectrometry detection device and method for active protein-small molecule interaction, which uses a mass spectrometry sensor to obtain tandem mass spectrometry data of non-covalent complexes of active protein incubated with small molecule compounds at different concentration points; performs non-linear curve fitting on the concentration sequence of the small molecule compounds based on the ion combination rate of the protein binding small molecules at each concentration point, to obtain an apparent dissociation constant of the active protein-small molecule interaction; determines a local conformation change parameter of the active protein induced by small molecule binding and a potential binding domain of the small molecule; and performs fusion evaluation on the binding strength and conformation influence degree of the protein-small molecule interaction based on the apparent dissociation constant, the local conformation change parameter and the potential binding domain, to output a stability grade of the protein-small molecule interaction. Based on the above scheme, non-denatured state mass spectrometry detection of protein-small molecule complexes in a liquid phase environment can be realized.

The present application relates to a method and system for evaluating drug effectiveness in the presence of a partially cured population, the method comprising the steps of: obtaining observational study sample data, determining proxy variables and covariates, and dividing the sample into a treatment group and a control group according to a treatment protocol; performing parameter estimation on the treatment group and the control group using a mixed cure model respectively to obtain a latent incurable rate and a survival function of non-cured persons for the treatment group and the control group; calculating a drug cure rate causal effect parameter based on the latent incurable rate of the treatment group and the control group; calculating a survival function of an incurable main layer based on the latent incurable rate of the treatment group and the control group and the survival function of non-cured persons; and evaluating the effectiveness of the drug based on the drug cure rate causal effect parameter and the survival function of the incurable main layer.

A computational system and method for digital modeling of cardiac cell membrane electrophysiology is disclosed. The system generates a digital model representing the cell membrane as a dielectric lipid bilayer comprising a capacitor element and a resistor element, where the resistor element is emulated by a plurality of ion channels. The system calculates cell membrane potential based on intracellular and extracellular concentrations of key ions, such as Na+, K+, Ca2+, and Cl-, using the Goldman-Hodgkin-Katz equation. Generation of cellular membrane action potentials is simulated by modeling the opening and closing of voltage-gated ion channels in response to triggering events. The model provides dynamic visualizations of the distinct phases of the action potential, including depolarization and repolarization, offering a high-fidelity tool for research and for enhancing the realism of training simulations in electrophysiology procedures.

The application discloses a kind of soft measurement modeling methods of fermentation process based on time sequence diagram network, belongs to the technical field of soft measurement of fermentation process.It includes the following steps: (1) data acquisition and integration: the penicillin fermentation process under different conditions is obtained, and the data is divided, collected and integrated;(2) data selection: the data is selected, the redundant useless data is removed and the causal diagram between variables is established;(3) modeling training: algorithm model is constructed, and learning training is carried out;(4) model prediction: the trained algorithm model is used for prediction, and the prediction result is given.The application proposes a kind of soft measurement modeling method of fermentation process based on time sequence diagram network, improves the prediction accuracy of key product quality of fermentation process;The method uses graph convolution network and long short-term memory, extracts data in time and space dimensions, increases the generalization of model;The method can accurately measure the key product quality of different fermentation processes.

The application discloses a kind of based on DNA methylation's construction method of biological age prediction model, the application is downloaded from GEO data website, derived from Chinese population, contains calendar age data, whole blood sample 450k methylation chip data, by the method of elastic network combined bootstrap, 31 methylation sites of modeling candidate are screened, multiple linear regression, support vector machine, random forest and gradient boosting regression tree are used to carry out preliminary construction and evaluation of model, then further filter methylation sites using full subset regression, obtain methylation age prediction model based on 18 methylation sites.Finally, using any provincial team natural population data, methylation age prediction model is optimized, and finally the biological age prediction model based on 18 methylation sites is obtained.The model is suitable for Chinese population, the number of methylation sites contained is less, is not affected by blood cell components, and the prediction accuracy is good.

We provide the system. [Solution] Means for automatically collecting biodata and chemical information, A means for analyzing the aforementioned data and predicting new drug candidate substances using a machine learning algorithm, A means for evaluating the characteristics of a new drug candidate by performing in silico simulations based on the prediction, A system that includes this.

A computer-implemented method of prioritizing biological targets is disclosed. The method includes receiving a selection of one or more categories of rankings; and determining, for each of a plurality of biological targets, a degree of consistency of the biological target with each selected ranking. The method further includes prioritizing the biological targets based on the degree of consistency; and outputting a representation of one or more prioritized biological targets.

This invention belongs to the fields of biomedicine and detection analysis technology, specifically relating to a predictive model for the blood concentration of antipsychotic drugs mediated by multiple gene SNP sites, its construction method, and its application. This invention screens genetic and non-genetic factors affecting the blood concentration of commonly used antipsychotic drugs and explores the influence of related factors on the in vivo metabolic process of antipsychotic drugs. Based on a nonlinear mixed-effects model, it integrates genetic and non-genetic factors to construct population pharmacokinetic models for antipsychotic drugs such as risperidone, quetiapine, clozapine, and olanzapine. Simultaneously, this invention also employs the Super Learner prediction algorithm to construct a predictive model for the steady-state trough concentration of antipsychotic drugs, providing a quantitative scientific reference for clinicians to determine the dosage for schizophrenia patients, thus possessing significant practical application value.

This application discloses a method for association analysis based on high-throughput dynamic phenotypes and genetic effects, relating to the field of agricultural bioinformatics. By fitting growth curves to high-throughput dynamic phenotype data from multiple time points, the dynamic changes in the growth cycle of a biological population are obtained. A joint likelihood function algorithm is then constructed to associate this dynamic vector with genetic effects, revealing genetic markers related to growth and development trends and processes. Compared to existing GWAS methods, which can only analyze static, single-time-point phenotype data and cannot uncover genetic loci regulating biological growth and development, this application, by performing association analysis on data collected from multiple consecutive time points, can more comprehensively reveal the evolutionary patterns of phenotypes over time, thereby achieving accurate analysis of genetic markers.

The application belongs to the technical field of genomic breeding, and provides a machine learning genomic selection system and method, which includes the whole process of data import, model selection, hyperparameter tuning, model training, evaluation and prediction. The platform integrates various machine learning algorithms, including kernel ridge regression, support vector regression, random forest, k-neighbor algorithm, and provides automatic hyperparameter optimization, model selection and result visualization functions. The platform also provides rich data visualization functions to help users better understand data and model results. The machine learning genomic selection platform of the application has the characteristics of high stability, simple operation and high integration, significantly reduces the technical threshold of genomic selection, enables breeding researchers without programming background to efficiently perform genomic data analysis, and promotes the wide application of machine learning technology in animal breeding.

The application discloses a phage construction model training method, a phage construction method and a terminal device. The training method trains a phage construction model through a phage sample, so that the phage construction model learns a mapping relationship between a sequence state of the phage and next element information of the phage, thereby enabling, in actual application, for a phage with missing tail proteins, the element sequence of the tail proteins of the phage to be filled through the trained phage construction model, so that a phage with a complete sequence is constructed, and efficient construction of the phage is realized.

The application provides a plasma exosome marker and application for early diagnosis of pancreatic cancer, utilizes a metabolomics and proteomics method, analyzes metabolites and proteins with significant differences in exosomes separated from plasma of pancreatic cancer patients, pancreatitis patients and normal persons, screens a series of biomarkers capable of early indicating a pancreatic cancer occurrence risk, and further screens a group of biomarkers to construct a pancreatic cancer diagnosis model, so that the pancreatic cancer diagnosis model can be used for conveniently and efficiently predicting whether an individual is suffering from pancreatic cancer, and meets clinical requirements.

Example systems and methods for use in trait development in agricultural crops are disclosed. An example computer-implemented method includes: identifying a plurality of proposed varieties of a crop, wherein each proposed variety of the plurality of proposed varieties includes a different genetic sequence than other proposed varieties of the plurality of proposed varieties and known varieties; predicting, using a trained model, a trait of interest for each proposed variety of the plurality of proposed varieties based on data included in a repository; selecting a proposed variety of the plurality of proposed varieties according to a harvest function based on a phenotypic gain; and causing seeds representative of the selected proposed variety of the plurality of proposed varieties to be directed to an experimental stage to evaluate the trait of interest for the selected proposed variety of the plurality of proposed varieties.

This invention discloses an active peptide that regulates the function of the intestinal epithelial mucus barrier, its preparation method, and its application. It belongs to the field of bioengineering technology. The amino acid sequence of the active peptide is shown in SEQ ID NO: 1, 2, 3, 4, 5, or 6. Using duck skin as raw material, this invention extracts a protein extract, collects low-molecular-weight peptide components through enzymatic hydrolysis and ultrafiltration fractionation, and freeze-dries the extract to obtain crude active peptide powder. The target active peptide is then screened and purified using an intestinal epithelial mucus barrier damage model. This active peptide can significantly improve mucin secretion levels, promote MUC2 gene expression, maintain goblet cell secretory function, restore the integrity of the mucus layer, and effectively improve the intestinal epithelial mucus barrier function damaged under inflammatory stimulation and oxidative stress, blocking pathogenic microorganisms and toxins from contacting epithelial cells. The active peptide obtained by this invention has a clear structure and defined function, providing a new approach for the high-value utilization of livestock and poultry by-products and the development of intestinal health products.

This invention discloses a fecal transplant donor efficacy prediction system and model construction method for adolescent depression, belonging to the field of combining biomedicine and artificial intelligence. It includes a detection module for obtaining quantitative detection results of preset microbial markers in fecal samples of the donor to be tested, including *Holmania fibrosum*; and a comparison module for comparing the quantitative detection results with preset thresholds to determine whether the donor to be tested is a high-efficiency or low-efficiency donor. The product provided by this invention has good feasibility and accuracy, and can effectively distinguish between high-efficiency and low-efficiency donors, providing a new diagnostic / predictive tool for clinical diagnosis.

This invention discloses a kit for detecting the methylation levels of ELOVL2, FHL2, and PDE4C genes and its applications, belonging to the field of biotechnology. The invention designs a kit for detecting the methylation levels of ELOVL2, FHL2, and PDE4C genes. The kit contains a combination of primers and probes for detecting methylated ELOVL2, FHL2, and PDE4C genes treated with bisulfite. By using the kit to detect the DNA methylation levels of ELOVL2, FHL2, and PDE4C genes, a methylation age prediction model based on DNA methylation levels is obtained. The predicted methylation age values ​​are relatively accurate and have a high degree of consistency with actual age, showing very promising application prospects.

This application discloses a low-scaling coarse-grained modeling method and related apparatus based on machine learning, belonging to the field of molecular dynamics simulation technology. The method includes: acquiring full-atom data of the target system; constructing a coarse-grained model to be learned; the potential function form of the coarse-grained model to be learned includes a potential energy expression describing polar correlation interactions, and the coarse-grained model to be learned is a model validated by full-atom data; using the full-atom data as the optimization target, the potential function parameters to be determined in the coarse-grained model to be learned are automatically iteratively optimized using machine learning methods until preset conditions are met, and the optimized coarse-grained model is output. While preserving key electrostatic and dipole physics mechanisms, this method significantly reduces the degrees of freedom of the coarse-grained model and reduces human intervention through a highly automated process, thereby improving the computational efficiency of complex system simulations and enhancing the model's accuracy, stability, and cross-system transferability.

This invention discloses an artificial intelligence-based method and system for designing bispecific antibodies, belonging to the fields of bioinformatics and antibody engineering technology. This invention uses the interface region between the parental monoclonal antibody and the monoantigen as the basis for determining key amino acid sites, and introduces virtual mutations to construct a candidate bispecific antibody sequence library. The candidate sequences are then input into an artificial intelligence structure prediction model to predict the 3D structure of the bispecific antibody and analyze antibody-antibody interactions. Through multi-round structure prediction and quantitative scoring, this invention achieves a systematic evaluation of the stability of the bispecific antibody and its binding ability to the two antigens, simulating actual bispecific antibody action scenarios and improving the reliability of the design results. This invention quantifies complex intermolecular interactions into a computable scoring system, enabling high-throughput virtual screening of bispecific antibodies, significantly shortening the design cycle, reducing R&D costs, and effectively improving the success rate of bispecific antibody design.

The application discloses a cell proportion prediction method and device fusing prior biological knowledge, and the method comprises the following steps: inputting mixed RNA expression data of a to-be-detected tissue sample into a pre-trained double-flow deconvolution model, wherein the double-flow deconvolution model comprises a feature extraction module and a feature fusion prediction module, the feature extraction module comprises two parallel branches: a deep neural network and a knowledge-driven sparse neural network, and the data processing process comprises the following steps: inputting the mixed RNA expression data into the deep neural network to extract a data-driven hidden layer high-dimensional feature vector; inputting the mixed RNA expression data into the knowledge-driven sparse neural network to extract a knowledge feature vector conforming to biological rules by using a pre-constructed knowledge mask; and inputting the hidden layer high-dimensional feature vector and the knowledge feature vector into a full connection layer and a Softmax activation function after splicing in the feature fusion prediction module, so as to obtain a prediction result of the proportion of each type of cell in the to-be-detected tissue sample.

The invention belongs to the technical field of molecular biology, and particularly discloses a theophylline aptamer and a virtual screening method and application thereof. The theophylline aptamer is selected from one or more of MUT9, MUT16, MUT67, MUT78 or MUT89. The invention further discloses a preparation method of the theophylline aptamer. The invention discloses a theophylline aptamer as well as a virtual screening method and application thereof, the theophylline aptamer is high in affinity and specificity, the virtual screening method remarkably improves the accuracy and efficiency of virtual screening, the theophylline aptamer can be quickly obtained at low cost, and a powerful calculation tool is provided for rational design and application of the aptamer.