33 results about "Metabolomics data" patented technology

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.

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 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 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.

This invention belongs to the field of biodetection technology, specifically disclosing a novel skin typing method, biomarkers based on skin surface metabolomics typing, and their applications. Based on population-level skin surface metabolite measurement data and through multi-dimensional scientific analysis, this application ultimately discovered that the skin samples of the subjects formed two distinct clusters. This demonstrates that differences in metabolomics clusters based on skin typing objectively exist. Furthermore, the two distinct clusters based on the aforementioned skin surface metabolomics data are named "Metabolic Type 1" or "Metabolic Type 2." Simultaneously, these two typing methods also exhibit differences in various skin condition indicators (e.g., sebum levels, actual age, wrinkles, freckles, etc.). By using the typing method provided in this application to study skin surface metabolomics substances, one or more metabolites can be used as biomarkers to identify the specific skin typing level and guide adjustments to the desired skin condition. This application overcomes the limitations of existing methods that define skin typing solely based on dry, oily, etc., providing more comprehensive guidance for practice.

Described herein are methods, such as multiomics methods, for assessing diseases, such as cancer. The multi-omics method can integrate proteomic data, transcriptomic data, genomic data, lipidomic data, or metabonomic data. The method screens for a disease or a disease state. Also described herein are methods for screening a disease or a disease state from a biological sample. The method may include assessing whether a nodule, mass, or cyst is cancerous.

The invention relates to the technical field of knowledge bases, in particular to an anesthetic dosage prediction method and system based on machine learning, and the method comprises the following steps: obtaining preoperative genome data, metabonomics data, intraoperative real-time vital sign data and operating room environment data of a patient, the genome data comprises CYP450 enzyme gene polymorphism data, and the metabonomics data comprises CYP450 enzyme gene polymorphism data; the metabonomics data comprises propofol metabolite concentration data, and the intraoperative real-time vital sign data comprises a BIS value, an MAP value and an HR value; according to the method, a dynamic hypergraph model containing drug nodes, gene nodes, metabolite nodes and environment nodes is constructed, the drug nodes and the gene nodes are connected through hyperedge to represent the regulation and control effect of drug metabolism genes on drug metabolism, the anesthesia safety and effectiveness can be improved, and the urgent clinical requirements for personalized and precise anesthesia are met.

This patent application discloses a method for constructing a risk prediction system for age-related hearing loss based on metabolomics, belonging to the interdisciplinary field of biomedicine and artificial intelligence. The method first acquires peripheral blood metabolomics data, pure-tone audiometry results, and age information of subjects, and then screens out age-related hearing loss patients with symmetrical hearing loss and healthy controls. Next, a hierarchical screening strategy is used to identify multiple core metabolites related to the age-related hearing loss state itself from massive amounts of data. Subsequently, based on these core metabolites, a predictive model is constructed using machine learning algorithms, and rigorously validated through independent internal and external clinical cohorts. This invention achieves early, accurate, and interpretable risk warning for age-related hearing loss, exhibiting excellent predictive performance.

The invention provides a metabolic marker composition for distinguishing health from breast cancer. Based on metabonomics data of blood plasma, a blood plasma metabolism marker combination for breast cancer diagnosis is screened, and a breast cancer diagnosis model with relatively high sensitivity and specificity is constructed in combination with a machine learning algorithm. The method has high sensitivity and specificity, samples are convenient to obtain, non-invasive performance is achieved, accurate screening of the breast cancer can be achieved, important help is provided for prevention of the breast cancer and reduction of the morbidity, and the method is suitable for large-scale population screening and long-term tracking in areas with shortage of medical resources.

The invention discloses an application of coumarin in preventing and treating bacterial wilt of cigars. The coumarin is applied to prevention and treatment of bacterial wilt of cigars. According to transcriptome and metabonomics data in the early stage, the inventor finds a stable and high-content organic compound coumarin in a bacterial wilt resistant cigar variety, cigars are used as an application crop, the high-incidence disease of cigars, namely bacterial wilt, is used as an application object, coumarin with different concentrations is investigated in detail, and the application amount of the coumarin is greatly increased. The prevention and treatment effect on the bacterial wilt of cigars is achieved by means of foliage spraying and root irrigation. Research finds that 15 mL of coumarin solution with the concentration of 20-80 mg / L needs to be applied to each cigar seedling, and the cigar bacterial wilt prevention and treatment effect is excellent.

The invention provides a metabolic marker composition for distinguishing benign thyroid diseases from thyroid cancer and application of the metabolic marker composition. Based on blood metabonomics data, a diagnosis model capable of accurately distinguishing benign thyroid diseases and thyroid cancer is constructed by screening a specific metabolic marker combination and combining a machine learning algorithm, and the model has relatively high sensitivity and specificity. Based on the constructed diagnosis model, whether the patient suffers from the thyroid cancer or the benign thyroid disease can be judged at the same time only through one-time detection, excessive examination of the patient suffering from the benign thyroid disease can be avoided, excessive diagnosis and treatment are reduced, and excessive panic of the patient is avoided. The thyroid cancer diagnosis model method provided by the invention has the advantages of convenience, economy and convenience in sample acquisition, and is more suitable for large-scale population screening and regular tracking in regions with shortage of medical resources.

This invention relates to the field of biomedical technology, and more particularly to a biomarker set, staging model, and their applications for chronic kidney disease. This invention utilizes metabolomics data analysis to obtain a biomarker set based on serum creatinine. Then, through methodological optimization, a staging model for chronic kidney disease is constructed. This staging model is also used for staging chronic kidney disease, and the results are accurate, specific, precise, and highly sensitive. It eliminates the false positive problem of traditional serum creatinine-based methods and removes the influence of gender factors. Staging chronic kidney disease based on this model is more consistent with the disease's progression characteristics.

The invention discloses a metabonomics data analysis method and system, and the method comprises the steps: obtaining metabonomics data of a biological sample under multiple conditions, and carrying out the filling of missing values through employing a data distribution characteristic fusion strategy; calculating a response intensity distribution difference value of each metabolite feature through sliding window local fluctuation analysis, and screening candidate difference metabolite features; sequencing the response values of the features under different conditions according to the size and fitting the response values into a feature response curve, and clustering by calculating the similarity of the curve to obtain a feature cluster; further, on the basis of the collaborative change intensity between the overall form and local synchronism fusion measurement features, a weighted network is constructed, community division is carried out, feature pairs with high collaborative intensity are screened from all sub-communities, and a potential biomarker combination is formed. According to the method, the whole-process analysis from data reconstruction, difference screening to collaborative marker identification is realized, and the accuracy, robustness and biological interpretability of single-sample metabonomics data mining are improved.

The invention discloses a fish early-stage sex identification method, which comprises the following steps: selecting early-stage larvae of a plurality of target fishes as samples, collecting phenotypic spectrum data, metabonomics data and epigenetic data of the early-stage larvae of each target fish, and obtaining actual sex labels after the target fishes grow into adult fishes; performing correction and processing to obtain a phenotype spectrum matrix, a standardized metabolism matrix and a methylation matrix; constructing a partial least square objective function of minimizing the regularization with the elastic net, and outputting a spectrum key feature matrix, a metabolism key feature matrix and a methylation key feature matrix; dividing into a training set and a test set; an MLP network model for target fish gender prediction is constructed, training is carried out, and a convergent MLP network model is output; and obtaining a new early larva sample of the target fish, executing the above steps, and determining the gender of the new target fish by using the predicted gender label.

The application relates to the technical field of bioinformatics data processing, and discloses a bioinformatics analysis system for human tissue lipid metabolomics data. A data deconstruction module extracts an initial variable projection importance sequence and an orthogonal loading matrix of each candidate lipid by using an OPLS-DA model; a biochemical mapping module constructs a biochemical homology physical penalty matrix based on lipid structure differences and an exponential decay function; an optimization module performs Hadamard multiplication on the loading covariance matrix and the penalty matrix, extracts a rank-one structure principal axis vector according to the EYM theorem through SVD; and a compensation module constructs a factor sequence, injects an initial sequence to perform weight adjustment, generates a final-state compensation importance sequence, and extracts core driving factors. The application can at least solve the problem that traditional discriminant analysis mostly adopts a method of stripping and discarding orthogonal variance, and thus ignores underlying adjustment information with physical correlation, resulting in incomplete identification of core regulation nodes.

This invention discloses a metabolomics-based method for predicting the efficacy of non-invasive pretreatment intervention (NAIC) in gastric cancer and specific metabolites, belonging to the field of biodetection technology. The method includes: obtaining plasma samples from gastric cancer patients before treatment, detecting the abundance of their metabolites, and obtaining baseline plasma metabolomics data; classifying patients into a response group and a non-response group based on post-treatment imaging assessment results; comparing the baseline metabolomics data of the two groups to screen for differentially expressed metabolites; using these differentially expressed metabolites as features, training the model using LASSO regression, determining the optimal parameters through cross-validation, and constructing an efficacy prediction model composed of specific metabolites and their regression coefficients; for the sample to be predicted, detecting the abundance of its specific metabolites and inputting them into the model to obtain a risk score, and predicting the patient's response to treatment by comparing it with a risk threshold. This invention enables accurate efficacy prediction before treatment, providing an important basis for individualized clinical treatment decisions.

The hypometric genetics (hMG) method enhances genetic discovery and prediction by leveraging quality control flags, such as below limit of quantification (BLQ), data in large-scale omics (e.g., metabolomics) studies. This approach transforms quality control flags into binary traits, which are then analyzed alongside continuous traits using advanced machine learning techniques. The method applies gene-based rare variant aggregation tests and joint multi-trait analysis to improve statistical power, particularly for rare variants and extreme phenotypes. By integrating previously underutilized data, hMG significantly increases statistical evidence for prioritized genes and predictive accuracy of genetic prediction, offering a novel solution to challenges in genetic analysis. The method's effectiveness has been demonstrated using NMR-based metabolomics data from the UK Biobank, showing improved statistical power in identifying trait-associated genes and enhanced accuracy in polygenic risk score modeling with consistent performance across diverse population groups.

The application provides a method for correcting batch effect of metabolomics data based on a generative adversarial network, relates to the cross technical field of bioinformatics and analytical chemistry, and comprises the following steps: generating a metabolomics data training set and a metabolomics data test set; constructing a neural network model based on an attention mechanism; constructing a generative adversarial network model; constructing a time series according to the metabolomics data training set, and constructing a time series model based on the time series; performing deep joint training on the constructed neural network model, generative adversarial network model and time series model; obtaining corrected metabolomics data; and performing quality evaluation on the corrected metabolomics data. The technical problem of complex nonlinear batch effect being difficult to effectively eliminate due to factors such as differences between multi-center experiments and fluctuations in instrument performance, thereby causing metabolomics data deviation and cross-batch incompatibility, is solved.

This invention relates to the field of rat coronary heart disease complicated with depression treatment and intervention technology, and discloses a rat coronary heart disease complicated with depression treatment and intervention system, including a multidimensional acquisition module and an intelligent intervention module. The system acquires experimental data, metabolomics data, and efficacy data of intervention programs from all rats through the multidimensional acquisition module, and classifies them into datasets. The intelligent intervention module calculates a disease index based on the multidimensional data, accurately quantifies the severity of symptoms, and achieves dual correlation verification between metabolic mechanisms and pathological phenotypes. The multidimensional correlation has high accuracy. The intelligent intervention module evaluates the treatment effect of each rat after receiving different intervention programs, generates a treatment index, and assesses the population coverage of each intervention program's effect, generating an effectiveness index. It determines the symptom level and efficacy level of each rat, outputs corresponding evaluation results and intervention suggestions, and optimizes the intervention program through population data feedback. The intelligent intervention treatment effect is excellent.

Provided herein in certain embodiments is an Al-assisted method based to assess progression of mild cognitive impairment or a risk of developing Alzheimer's disease (AD) based on proteomic and metabolomic data of one or more samples obtained from a human subject via a portal. The novel portal described herein is based on a trained computational model for assignment of a dataset into three endotypes that is comprehensive enough to provide accurate assessments of AD risk and respective treatments for each, yet simple enough to avoid excessive energy usage demand on clinics and hospitals.

The invention belongs to the field of metabonomics research, and particularly relates to a method for processing direct injection mass spectrometry data, which comprises the following steps of: acquiring the direct injection mass spectrometry data containing mass-to-charge ratio and intensity information by a mass spectrometer through a direct injection mass spectrometry technology, and determining the mass-to-charge ratio of the direct injection mass spectrometry data based on mass-to-charge ratio matching and signal-to-noise ratio judgment. Background noise is removed from the direct injection mass spectrum data; when a plurality of samples are processed, performing peak alignment on the mass spectrum data of the plurality of samples; based on the mass difference and theoretical isotope intensity distribution, identifying isotope peaks in the mass spectrum data, and distinguishing single isotope peaks from isotope clusters; metabolite annotation is performed on the mass spectrometric data based on user-selected ion patterns, adduct types, and molecular weight tolerances. According to the method, four core modules of background noise removal, peak alignment, isotope peak recognition and metabolite annotation are integrated, and the key technical problem that a traditional LC-MS metabonomics data analysis method cannot be directly applied due to lack of retention time dimensions is solved.

The application discloses a kind of metabolomics data analysis method and system, method includes: obtaining the metabolomics data of biological sample under multiple conditions, adopt the strategy of fusion data distribution characteristics to carry out missing value filling;The response intensity distribution difference value of each metabolite feature is calculated by sliding window local fluctuation analysis, and candidate differential metabolite feature is screened;The response value of each feature under different conditions is sorted by size and fitted as feature response curve, and clustering is carried out by calculating curve similarity to obtain feature cluster;Further based on the overall morphology and local synchronism fusion degree of measure between features, construct weighted network and carry out community division, from each subcommunity High coordination intensity feature pair is screened in community, and potential biomarker combination is formed.The application realizes the whole process analysis from data reconstruction, difference screening to collaborative marker identification, improves the accuracy, robustness and biological explainability of single sample metabolomics data mining.

The invention discloses an intelligent response type forage grass stress resistance and salt tolerance regulation and control system, and the system comprises a machine vision module which is used for carrying out the saline-alkali stress recognition of a leaf image of awnless brome planted in saline-alkali soil through a trained deep learning model, and obtaining the saline-alkali stress level of the awnless brome; the metabonomics data module is used for performing metabolite analysis on nutritional organs of awnless brome by utilizing a chromatography-mass spectrometry technology and a random forest algorithm to obtain a key metabolite set of the awnless brome in the saline-alkali soil; the growth trend prediction module is used for analyzing the key metabolite set and the soil environment data set of the saline-alkali soil by using the constructed multivariable regression model to obtain a growth state prediction result of the awnless brome in the saline-alkali soil; and the soil regulation and control module is used for regulating a water and fertilizer management strategy of the saline-alkali soil according to the growth state prediction result and the saline-alkali stress level of the awnless brome. The stress resistance of the awnless brome in the saline-alkali soil can be improved.

The application provides a metabolomics data processing method based on a graph theory strategy, and belongs to the technical field of bioinformatics and analytical chemistry. First, metabolomics original peak data of multiple biological samples is acquired; quality cleaning is performed on the original peak data to obtain a peak set after cleaning, each peak being composed of mass-to-charge ratio, retention time and peak intensity to form multi-dimensional characteristic data; a peak matching graph is constructed based on the multi-dimensional characteristic data, graph nodes representing peaks, and graph edge weights reflecting the comprehensive similarity between peaks of any two different samples, the similarity being obtained by weighted fusion of normalized similarities of mass-to-charge ratio, retention time and peak intensity; based on the peak matching graph, an effective matching group is generated by depth-first search under the conditions of meeting basic similarity and chromatography-mass spectrometry continuity and high instrument resolution capability advanced constraints. The application significantly improves the accuracy and robustness of cross-sample metabolite peak matching, and effectively supports large-scale, multi-batch complex metabolomics data analysis.

This invention provides a method for comparative metabolomics deduplication and compound selection based on high-resolution mass spectrometry signals. By collecting primary and secondary mass spectrometry metabolomics data of a target strain, a reference strain, and empty culture medium, and using the metabolomics data of the reference strain and empty culture medium as controls, irrelevant interfering compound signals in the target strain's metabolomics are deduplicated, retaining and selecting genuine secondary metabolite signals. Compared to deduplication based directly on mass-to-charge ratio, this invention, through accurate molecular weight calculation based on adducts followed by database comparison, can more accurately eliminate identical compound signals at the primary mass spectrometry level. Furthermore, the secondary mass spectrometry comparison of this invention is based on fragment matching, independent of fragment intensity, for structural analogue identification, resulting in stable screening results. This method is of significant importance for identifying strains that are difficult to genetically manipulate and for discovering recessive metabolites with low expression levels that are difficult to detect using conventional methods.

The invention provides a metabolic marker composition for distinguishing kidney benign diseases and kidney cancer and application of the metabolic marker composition. Based on the metabonomics data of the plasma sample, the diagnosis model capable of distinguishing the kidney benign disease and the kidney cancer is constructed through a machine learning algorithm, and powerful support is provided for early noninvasive diagnosis and individualized treatment strategy formulation of the kidney cancer. The method is simple and convenient in sample collection, small in wound, high in sample stability and easy in standardized collection, is suitable for early screening and risk stratification of kidney diseases of a large scale of people, particularly provides an effective kidney cancer auxiliary diagnosis tool for areas with relatively deficient medical resources, and realizes accurate distinguishing of kidney benign diseases and kidney cancer.

The present application relates to the technical field of diagnosis markers and diagnosis methods for mental illness of children and adolescents, in particular to a plasma metabolic diagnosis marker and a diagnosis system for depression of children and adolescents. The plasma metabolic diagnosis marker comprises at least one of cyclic adenosine monophosphate, methylpyridine, unsaturated undecylenoyl carnitine, histidinol, prolyl hydroxyproline, tryptophan and glucuronic acid. The present application analyzes non-target metabolomics data, and determines seven candidate diagnosis markers for depression of children and adolescents from common differential metabolites. Then, through targeted metabolomics research, diagnosis markers and their combinations with ideal diagnosis effects are determined, and a diagnosis model is constructed. The gender factor is also included in the diagnosis model to further improve the diagnosis effect. The present application can solve the technical problem that the reliability and promotion potential of the plasma metabolic biomarkers for depression of children and adolescents in the prior art are not ideal, and has an ideal promotion and application prospect.

The invention relates to the technical field of biomedical treatment, in particular to a marker set for chronic kidney diseases, a staging model and application of the marker set and the staging model. According to the method, a marker set based on serum creatinine is obtained through metabonomics data analysis, then the staging model for staging the chronic kidney disease is constructed through methodological optimization, the staging model is used for staging the chronic liver disease, the result is accurate, specificity, precision and sensitivity are high, and the method is suitable for clinical application. The original false positive problem based on serum creatinine indexes is solved, the influence of gender factors is eliminated, and chronic kidney disease staging according to the staging model is more in line with the development characteristics of diseases.

This application discloses a multidimensional glucose index-driven interpretable AI-based weight management decision-making and intervention method, including the following steps: S1, acquiring multimodal data related to weight management for the target individual, including at least continuous glucose monitoring data, gut microbiota metabolomics data, behavioral data, and basic clinical indicators. Then, by integrating continuous glucose monitoring-derived indicators, gut microbiota metabolomics data, behavioral data, and basic clinical indicators, a multimodal standardized feature set is constructed, significantly improving the predictive accuracy of weight and body fat percentage changes. The SHAP value interpretability tool is used, providing both global and local interpretability, quantifying the positive and negative contributions of each feature to the prediction results, automatically prioritizing influencing factors based on the interpretability analysis results, and supporting dynamic report updates based on individual implementation status, thereby improving the long-term effectiveness of weight management.

The application discloses a four-dimensional metabolomics data processing method. The method comprises the following steps: acquiring M secondary mass spectrum diagrams of each sample in N samples; acquiring a target precursor ion from a precursor ion data frame corresponding to a precursor ion data frame index in the secondary mass spectrum diagram; determining an ion drift outflow peak and a chromatographic outflow peak of the secondary mass spectrum diagram according to a plurality of primary mass spectrum data points of the target precursor ion in a target ion data frame; and generating four-dimensional information of a four-dimensional peak of each secondary mass spectrum diagram according to a mass-to-charge ratio of the primary mass spectrum data point, an ion drift value of an ion drift peak vertex in the ion drift outflow peak, a chromatographic retention time of a chromatographic peak vertex in the chromatographic outflow peak, and an ion signal intensity in a chromatographic retention integral range in the chromatographic outflow peak. Therefore, the sensitivity of the four-dimensional peak detection algorithm can be improved, substances separated by LC-IM-MS can be fully converted into signals in subsequent metabolite qualitative and quantitative analysis, and the coverage of four-dimensional metabolomics identification is improved.