90 results about "Metabolomics data" patented technology

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.

Described herein are methods such as multi-omic methods for assessing a disease such as cancer. The multi-omic methods may integrate proteomic, transcriptomic, genomic, lipidomic, or metabolomic data. The method screening diseases or disease states. Also described herein are methods for screening for diseases or disease states from biological samples. The methods may include assessing whether a nodule, mass, or cyst is cancerous.

The invention discloses a metabonomics data batch correction method based on multi-kernel learning, and belongs to the cross technical field of bioinformatics and analytical chemistry. According to the method, the multi-kernel learning technology is utilized, the advantages of different kernel functions are fused in a self-adaptive mode, a model conforming to data reality is constructed, complex drift characteristics of metabolite signals are accurately captured, and efficient and accurate normalization processing of metabonomics data is achieved. Compared with traditional data standardization methods such as SVR and LOESS, the method has the advantages that the performance is excellent in the aspect of reducing the metabolite peak intensity variability, and the data stability is remarkably improved. In the subsequent multivariate statistical analysis, the classification accuracy is greatly improved, the comparability among different batches of data is also remarkably enhanced, reliable data support can be provided for discovery of disease biomarkers, and the method plays a key role in large-scale metabonomics research.

The invention relates to the technical field of biomedicine, in particular to a hepatic cell differentiation degree evaluation method based on multi-omics data, which comprises the steps of sample collection and preprocessing, transcriptomics, proteomics and metabonomics data analysis, multi-omics data integration and modeling and result output. By integrating multi-level biological information, a multi-dimensional scoring model is constructed, the liver cell differentiation state is quantitatively evaluated, and a standardized grading system is provided for clinic. The method can solve the limitation of single omics analysis, improves the evaluation accuracy and reliability, has universality, can be popularized to other malignant tumor research, and assists precise medical development.

The invention relates to a construction method of a metabolic syndrome phlegm syndrome lipid metabolism characteristic diagnosis model. The construction method comprises the following steps: S1, acquiring a serum sample; s2, carrying out lipid metabolite analysis through a full-quantitative lipidomics formula; s3, carrying out primary screening on differential metabolites; s4, optimizing the characteristic indexes by using a random forest algorithm and stepwise regression; and S5, constructing a differential metabolite diagnosis model through Logistic regression analysis. By analyzing differential lipid metabolites of patients with MetS phlegm syndromes and non-phlegm syndromes, the invention reveals that abnormal accumulation of lipid and lipid metabolites may be the core pathological parenchyma of MetS phlegm syndromes. By integrating lipid metabonomics data, using a random forest algorithm, stepwise regression and other methods to screen feature difference lipid metabolites and construct a MetS phlegm syndrome specific diagnosis model, a novel combined biomarker and evidence-based basis are provided for early diagnosis of MetS phlegm syndromes, and a scientific basis can also be provided for objective diagnosis of traditional Chinese medicine phlegm syndromes.

The invention discloses a metabonomics data batch effect correction method based on deep learning. The metabonomics data batch effect correction method comprises two stages of intra-batch correction and inter-batch correction. In the correction stage in batches, an SERRF method is adopted for modeling the sampling sequence of the QC samples, the systematic drift effect is recognized and corrected, and data deviation caused by factors such as the sampling sequence in the same batch is eliminated. In the inter-batch correction stage, firstly, the mass center of each batch is calculated, and the pairwise correction sequence is determined based on the distance between the batches; on the basis of a combined framework of a generative adversarial network and an auto-encoder, systematic deviation among batches is reduced through adversarial training, mutual nearest neighbor alignment loss is introduced, and biological information loss caused by over-correction is avoided. According to the batch correction method of the multi-batch data pairwise correction sequence, the advantages of the random forest and the deep adversarial alignment network are combined, and a two-stage joint correction framework is constructed. While real metabolic differences of biological samples are reserved, errors in data are eliminated, and reliable data support is provided for metabonomics research.

The invention discloses an intelligent scheduling and resource management system for a microbiological detection laboratory, and relates to the technical field of intelligent management, and the system comprises a resource distribution module which carries out the space-time resource distribution of biological safety constraints through a quantum computing adaptive modeling method, generates a task scheduling optimization problem, carries out the optimal solution search through a quantum annealing algorithm, and carries out the optimization of the optimal solution; obtaining a reference resource scheduling scheme; the scheduling module is used for analyzing through a preset public health risk monitoring threshold value, and adjusting resource application and allocation by utilizing a dynamic resource recombination algorithm to obtain a resource scheduling instruction; the collaboration module is used for predicting a laboratory reagent consumption trend by adopting a metabonomics data analysis method, constructing a cross-laboratory emergency resource sharing path through a GAN network and outputting an emergency resource sharing scheme; according to the method, the biosecurity isolation rule is converted into the quantum bit coupling relation through quantum computing adaptive modeling, and the resource scheduling efficiency and the instrument utilization rate of high-grade experiments are improved.

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 discloses a traditional Chinese medicine analysis and identification method and system based on clustering analysis, and relates to the technical field of traditional Chinese medicine analysis, and the method comprises the following steps: S1, collecting a chemical fingerprint spectrum, a microscopic image, metabonomics data and geographical indication information of a traditional Chinese medicine sample; according to the traditional Chinese medicine analysis and identification method and system based on clustering analysis, through a dynamic weight distribution mechanism and a high-order nonlinear collaborative characterization technology, the core problem of multi-modal data fusion failure in traditional Chinese medicine identification is effectively solved; the dynamic weight distribution network adaptively adjusts the contribution degree of each modal based on real-time entropy fluctuation and sample density, so that the identification precision under complicated scenes such as related species and processed product difference is remarkably improved; the high-order tensor fusion space is combined with adversarial generation training, the characterization limitation of a linear kernel function on component-form-effect nonlinear correlation is broken through, and the clustering boundary better fits the overall characteristics of traditional Chinese medicine.

The invention discloses a risk assessment method, system and equipment for idiopathic pulmonary arterial hypertension, and belongs to the field of pulmonary arterial hypertension. According to the method, SNP data containing genotypes and effect values, protein marker expression quantity, metabonomics and clinical data are obtained, the SNP effect values are corrected based on linkage imbalance reference information, and PRS is calculated in combination with the genotypes; constructing a protein expression score by utilizing the site effect value and the expression quantity of the pQTL, and fusing the protein expression score with the PRS to form a target PRS; carrying out dimensionality reduction on metabolome data by adopting sparse coding, extracting sparse coefficients of IPAH related metabolic pathways, and converting the sparse coefficients into metabolic pathway scores; converting the clinical indexes into clinical risk scores; based on the clinical parameter distribution target PRS, the metabolic pathway score and the weight coefficient of the clinical risk score, calculating a risk assessment value; and finally, matching the evaluation value with a preset risk threshold value, and outputting a risk evaluation level. And the IPAH risk assessment accuracy of common people is improved.

The invention relates to the technical field of bioinformatics, in particular to a method for typing acute respiratory distress syndrome biological subtypes. The method comprises the following steps: a) acquiring multi-omics data and carrying out standardized preprocessing; the multi-omics data comprises transcriptomics data, proteomics data and metabonomics data of a biological sample source; b) constructing a similarity network of each group by using a similarity fusion network (SNF), and obtaining a uniform sample similarity matrix through multi-group network fusion and iteration; multiple collaborative principal component analysis (MCIA) is adopted to carry out dimension reduction on multi-omics data so as to realize visualization of a clustering result; carrying out multi-omics joint discrimination modeling under the guidance of SNF clustering by using a data integration analysis (DIABLO) method so as to identify key feature variables; and carrying out biological subtype classification based on the clustering result of the steps.

The invention discloses a radiotherapy sensitivity and toxic and side effect detection system based on multiple omics, and relates to the technical field of radiotherapy, and the system comprises the steps: obtaining genomics data, proteomics data and metabonomics data of a patient at different stages, and generating a patient multi-omics time series data set; based on the patient multi-omics time sequence data set, extracting patient multi-omics time sequence features, and constructing a patient radiotherapy sensitivity dynamic prediction model; combining clinical manifestation and treatment history in clinical data of the patient, generating an adversarial network by utilizing a cGAN condition, generating toxicity simulation data under different radiotherapy doses, and establishing a patient toxic and side effect risk prediction model; and according to the patient radiotherapy sensitivity dynamic prediction model and the toxic and side effect risk prediction model, obtaining an optimal radiotherapy dose interval of the patient, and generating a patient personalized radiotherapy digital twinning scheme. The method has the beneficial effects that the treatment safety and effect of patients are improved, and the method has higher clinical application value and personalized treatment potential.

The invention relates to the technical field of health risk prediction, in particular to a metabonomics-radiomics prediction method for chronic subdural hematoma recurrence risk, which comprises the following steps: acquiring a CT image and extracting edge gray fluctuation, constructing fluctuation parameters in combination with metabolome data, screening coordination characteristics to generate a risk combination, and predicting a chronic subdural hematoma recurrence risk. Feature pairs consistent in trend are extracted to form a collaborative channel, and a feature matrix is constructed to generate an input vector set; according to the method, disturbance features are extracted through a CT image edge gray level path, a cross-modal fluctuation trend comparison mechanism is established in combination with patient brain metabolism indexes, biological consistency between the features is enhanced, feature combinations with uncoordinated changes are eliminated, feature pairs with collaborative structure and function trends are screened, and a linkage path is constructed. The evolution relation from structural disturbance to metabolic response is reflected, channel data sorting and recombination improve the difference of input characteristics, the stability and accuracy of recurrence discrimination are enhanced, and the systematicness and interpretability of risk assessment are improved.

The invention discloses a method for carrying out single-objective optimization on plant tissue culture based on artificial intelligence, and relates to the field of plant tissue culture, and the method comprises the following steps: building a plant tissue culture environment, and collecting and preprocessing environmental parameters; metabonomics data of plant tissues are measured through mass spectrum and liquid chromatography analysis, and a single-target optimization culture target is set in combination with environmental parameters; based on the environmental parameters and the metabonomics data, constructing a metabonomics analysis model through a random forest algorithm, and extracting a preliminary optimization path; according to the preliminary optimization path, using a sparse optimization algorithm to minimize plant culture resources, and obtaining a minimum resource scheme; plant tissue culture is carried out by using a minimum resource scheme, environmental parameters and metabonomics data are continuously collected, and metabonomics analysis model parameters are updated. By comprehensively utilizing artificial intelligence, metabonomics and an optimization algorithm, intelligence and precision of plant tissue culture and efficient utilization of resources are improved.

The invention discloses a space transcriptome and space metabolome integration method based on deep learning. The method comprises the following steps: firstly, acquiring original space transcriptomics data and original space metabonomics data of a biological tissue, and performing data preprocessing to obtain a preprocessed biological tissue data set; then, aligning space metabonomics data in the preprocessed biological tissue data set to space transcriptomics data, unifying data resolution, obtaining corrected space metabonomics data, and updating the biological tissue data set; and finally, generating to-be-integrated sample data from the newest biological tissue data set, inputting the to-be-integrated sample data into the spatial multi-omics data integration model, and outputting final joint embedding by the model, so that cross-modal and cross-sample effective integration of the data can be realized. According to the method, the problems of form and resolution inconsistency and batch effect caused by technical difference of ST and SM data are solved, and high-precision and interpretable spatial multi-omics integration analysis is realized.

The invention discloses a newborn rare disease intelligent screening and diagnosis system based on multi-omics data fusion, relates to the technical field of medical data processing, and aims to solve the technical problems that a traditional diagnosis method is long in time consumption and low in accuracy and cannot meet the requirement for rapid and accurate diagnosis of newborn rare diseases. The data acquisition module is used for acquiring clinical omics data from a doctor's advice database through a neonatal medical record, and acquiring blood samples to acquire gene data and metabonomics data and marking when the medical record records that the neonatal suffers from a rare disease; the data processing module is used for preprocessing the clinical omics data according to the neonatal medical record to obtain a training set and a test set, and preprocessing the gene data and the metabonomics data according to the blood sample; and the diagnosis module is connected with the data processing module. By constructing a three-level modular architecture, the problem of missed diagnosis caused by traditional multi-source data isolated analysis is effectively solved, and the accuracy rate of newborn rare disease diagnosis is remarkably improved.

Described herein are methods such as multi-omic methods for assessing a disease such as cancer. The multi-omic methods may integrate proteomic, transcriptomic, genomic, lipidomic, or metabolomic data. The method screening diseases or disease states. Also described herein are methods for screening for diseases or disease states from biological samples. The methods may include assessing whether a nodule, mass, or cyst is cancerous.

The invention discloses a method for analyzing component difference between Maotai-flavor yeast and yeast-making wheat by using non-targeted metabonomics. The method comprises the following steps: S1, pre-treating a sample; s2, detecting each sample by adopting the combination of an ultra-high performance liquid chromatograph and a quadrupole time-of-flight mass spectrometer; s3, acquiring original data, performing peak extraction, peak alignment and normalization by adopting metabonomics data analysis software, and evaluating the overall difference and intra-group variation among sample groups by adopting principal component analysis; constructing a grouping discrimination model through discriminant analysis of an orthogonal partial least square method; determining differential metabolites of the two groups of samples by taking variable projection importance (VIP) greater than 1 and statistical significance P less than 0.01 as a primary screening standard and combining a secondary screening standard that the difference multiple log2FC greater than 2 is up-regulated and the difference multiple log2FC less than-2 is down-regulated; s4, performing compound structure and category annotation on the screened differential metabolites through a metabolome database; and performing pathway enrichment analysis by adopting a statistical test method, and determining a core metabolic pathway enriched by the differential metabolites.

The present application relates to a system for assessing the degree of senescence, which assesses the degree of senescence of a cell by calculating a score of a cell metabolome state by taking into account metabonomics data of the cell. The system is especially suitable for cosmetic efficacy evaluation, anti-aging drug screening and other scenes, can shorten the research and development period in practical application, and has significant industrial application value.

The present application relates to a kind of aerosol delivery system toxicology scoring method and storage medium, method includes: setting target group, the test biological of target group is in aerosol exposure environment;Blood physiological index and multi-omics data of control group are obtained, the test biological of control group is in air exposure environment;Blood physiological index of target group is measured, and first toxicology score is determined according to the difference of blood physiological index of target group and control group;Multi-omics data of target group is measured, and second toxicology score is determined according to the difference of multi-omics data of target group and control group;According to first toxicology score and second toxicology score, determine the comprehensive toxicology score of target group;Blood physiological index includes blood biochemistry, coagulation function and hematology index;Multi-omics data includes transcriptomics, proteomics and metabolomics data.The present application is evaluated from macroscopic physiological level and microscopic molecular level by the above setting, the toxicological effect of aerosol such as electronic cigarette liquid etc. is evaluated comprehensively.

The present application provides a kind of based on machine learning's mark metabolite model prediction method of optimal harvest period of Chinese yam, steps are as follows: collecting Chinese yam samples of different harvest periods, obtain metabolomics data by analyzing Chinese yam samples through metabolomics technology;Metabolomics data are preprocessed;The feature related to the growth period of Chinese yam is obtained by using machine learning algorithm to select potential marker metabolite;LASSO regression method is used to screen potential marker metabolite to construct marker metabolite prediction model;The area under ROC curve is used to verify the constructed marker metabolite prediction model;The metabolomics data of new Chinese yam are input into the marker metabolite prediction model to obtain model score, and whether Chinese yam is suitable for harvesting is judged according to model score.The present application can accurately predict the optimal harvest period of Chinese yam, eliminate subjectivity and experience dependence, improve scientificity, reduce external environmental influence, realize Chinese yam production capacity maximization, and provide reliable technical support for agricultural production.

The invention relates to the technical field of forest tree hybridization, and discloses a forest tree hybridization parent precise matching method based on multi-omics analysis, which comprises the following steps: S1, obtaining multi-omics data: performing genome sequencing, transcriptome analysis, proteomics analysis and metabonomics analysis on forest tree population individuals; a plurality of omics data such as genetic variation sites, gene expression quantity, protein expression abundance and metabolite spectrums are obtained. According to the forest tree cross parent accurate matching method based on multi-omics analysis, forest tree genetic characteristics are analyzed comprehensively through multi-omics data, genomics, transcriptomics, proteomics and metabonomics data are deeply fused, genetic factors closely associated with target traits are accurately identified, and the accuracy of forest tree cross parent matching is improved. According to the method, the scientificity of parent matching in forest tree cross breeding on the molecular level is remarkably improved, the fuzziness and uncertainty of traditional judgment only according to phenotype and experience are abandoned from the source, the parent matching accuracy is greatly improved, and the breeding work is more targeted and efficient.

According to the metabonomics database of Artocarpus nanchuanensis tissues, the Artocarpus nanchuanensis tissues comprise leaves, fruits, seeds and bark tissues, and the fruits comprise green ripe fruits, color-changed fruits and completely ripe fruits. According to specific metabolites of all the tissues, 1671 metabolites in Artocarpus nanchuanensis are detected by combining an extracting solution sample obtained by treating all the tissues with a UPLC-MS / MS detection method, the test result is accurate and comprehensive in coverage, so that a more comprehensive metabonomics database of Artocarpus nanchuanensis is constructed, and the accuracy of the metabonomics database of Artocarpus nanchuanensis is improved. A basis is provided for subsequent qualitative and quantitative analysis of metabolites of different tissues of Artocarpus nanchuanensis, and the method has important significance in research of metabolic characteristics of Artocarpus nanchuanensis, development of medicinal value and species protection.

The invention relates to a high-resolution mass spectrum-deep learning driven metabonomics data automatic analysis method, and belongs to the technical field of data science. The method comprises the following steps: acquiring an original mass spectrum data file through a liquid chromatogram-high resolution mass spectrum coupling technology; performing peak extraction, peak alignment and de-noising processing on the original mass spectrum data file through an automatic pre-processing module, and generating a standardized metabolite characteristic quantitative matrix based on a characteristic extraction module; based on the standardized metabolite characteristic quantitative matrix and secondary mass spectrum information in the original mass spectrum data file, automatically identifying metabolite characteristics through an adaptive identification model; and for the identified metabolites and the corresponding metabolite characteristic quantitative matrix, automatically matching optimal results in a database, and obtaining corresponding molecular formula compositions and compound names. The metabolite identification speed and precision are obviously improved, and errors possibly caused by manual intervention are reduced.

The present invention discloses a biomarker combination for early diagnosis of latent mastitis in dairy cows and its application, and relates to the field of biomedicine technology. The biomarker combination includes digalacturonic acid and N-ε-methyl-L-lysine. The present invention also provides the application of a reagent for detecting the concentration of the biomarker combination in the preparation of a product for early diagnosis of latent mastitis in dairy cows. The present invention combines metabolomics data analysis to develop a biomarker combination for early diagnosis of latent mastitis in dairy cows. The early diagnosis method constructed based on the biomarker combination can effectively identify early metabolic disorders of SCM by measuring the concentration changes of digalacturonic acid and N-ε-methyl-L-lysine, can provide accurate and efficient support for dairy cow health management, and has broad application prospects.

Described herein are methods for identifying a biological state, such as pancreatic cancer, in a subject. For example, a method may include obtaining protein data, transcriptomic data, genomic data, lipidomic data, or metabonomic data of a subject and identifying a likelihood that the subject has pancreatic cancer. The present disclosure includes methods of making and using a classifier.

The present application relates to the technical field of bioinformatics and disease prediction, and discloses a gout risk prediction model construction method based on metabolomics data. The method comprises the following steps: constructing a metabolite space layout atlas containing metabolite coordinates and connection strength based on historical gout case data; projecting real-time metabolomics data of a target individual to the atlas, calculating the spatial deviation amount of the distribution of the target individual from the benchmark structure, and generating an individual metabolic space deviation report; extracting a metabolite cluster deviating beyond a threshold and analyzing the topological characteristics thereof to generate a metabolic abnormality topological feature set; inputting the feature set into a pre-constructed metabolic state evolution network, simulating a disturbance propagation process, and outputting a metabolic state evolution path and potential imbalance nodes; and finally integrating the above information to generate a gout risk level prediction model. The present application quantifies the overall structural deviation of the metabolic network and simulates the dynamic evolution thereof, thereby achieving earlier and more accurate mechanism-based prediction of gout risk.

The present application relates to a kind of metabolic pathway extension network based on structural feature automatical establishment method, belong to bioinformatics and computational chemistry technical field.The purpose is to solve the problem of existing KEGG database incomplete, annotation lag of metabolite.Method includes: obtaining KEGG skeleton and modified group construction seed library;Through substructure matching and set difference calculation to determine the attribution of derivative;Iterative expansion forms structure association set;Combining stereochemistry rule and mass difference verification screening derivative;Merging KEGG reaction and extended relationship constructs network, and remove redundant path by graph theory method, finally output extended pathway network.The method greatly improves the annotation rate of metabolite pathway, supports in-depth functional analysis of metabolomics data.

The invention discloses a method, a device, a medium and equipment for predicting low-temperature tolerance of saccharomyces cerevisiae, and relates to the technical fields of biotechnology, microbial engineering and computer. The method comprises the following steps: acquiring transcriptomics and metabonomics real-time data of saccharomyces cerevisiae; screening transcriptomics data, and reserving data with high inter-batch correlation and low variable coefficient; screening metabonomics data, and reserving data with small monitoring peak area and batch effect passing PCA (Principal Component Analysis) inspection; screening out gene characteristics of the transcriptomics data and metabolite characteristics of the metabonomics data according to the significance level and a logarithmic 2-time change threshold; processing the features, and integrating the features into a biomarker feature set; and inputting the feature set into a trained random forest model, and predicting a low-temperature tolerance classification result of the saccharomyces cerevisiae. According to the method, the capturing capability of the model on complex biological characteristics is remarkably improved, a multi-layer mechanism of low-temperature tolerance is disclosed, and the prediction precision of the low-temperature tolerance of the saccharomyces cerevisiae is effectively improved.

The present application relates to a kind of high-resolution mass spectrometry-depth learning driven metabolomics data automatic analysis method, belong to data science technical field.The method includes: obtaining original mass spectrum data file by liquid chromatography-high resolution mass spectrometry technique;Through automatic pre-processing module, peak extraction, peak alignment, denoising processing are carried out to original mass spectrum data file, and standardization metabolite feature quantitative matrix is generated based on feature extraction module;Based on the standardization metabolite feature quantitative matrix and the secondary mass spectrum spectrum information in original mass spectrum data file, metabolite feature is automatically identified by adaptive identification model;The metabolite and corresponding metabolite feature quantitative matrix identified are matched with the best result in database automatically, and the corresponding molecular formula composition and compound name are obtained.The speed and accuracy of metabolite identification are significantly improved, and the error caused by manual intervention is reduced.