9 results about "Glycomics" patented technology

The application discloses a diabetes patient biological age calculation method based on glycosylation markers and a multi-task deep learning framework, and belongs to the technical field of bioinformatics and medical data processing technology, and the method comprises the following steps: obtaining glycomics feature data and clinical examination data of diabetes patients; preprocessing the data; obtaining a feature set by adopting a multi-method screening and fusion strategy; constructing a deep learning model comprising a multi-modal feature fusion module and a multi-task learning module for training, wherein the multi-task learning module comprises a main task of aging clock regression and at least one auxiliary task; and outputting biological age prediction values and age acceleration evaluation results of individuals by using the trained model. The method realizes deep fusion of multi-modal biological data and exclusive modeling for the diabetes population, can accurately quantify the biological aging process and the deviation degree relative to the calendar age, and provides an objective quantitative tool for clinical evaluation.

The application discloses an enzyme method for synthesizing unnatural sugar and application thereof, and belongs to the technical field of organic chemical synthesis. The unnatural sugar comprises an unnatural sugar obtained by introducing an acyl group into a hydroxyl group at the 6th position of a bare sugar through an acylation reaction; and the bare sugar is obtained by performing derivatization modification on an amide site of a natural sugar. The synthesis method uses a SubC enzyme as a catalyst to realize selective acylation of the hydroxyl group at the 6th position of the bare sugar, and synthesizes the unnatural sugar with an ideal yield. The unnatural sugar can be applied to glycomics metabolic labeling and mass spectrometry analysis as a marker, has excellent biological safety, specificity and labeling efficiency, can avoid non-specific labeling, and has no obvious cytotoxicity.

The invention discloses a hepatocellular carcinoma patient prognosis combined prediction model and a construction method and application thereof. The construction method comprises the following steps: acquiring baseline clinical indexes and serum N-carbohydrate chain characteristic data of a patient; key clinical indexes and key glycomics markers are screened out through feature engineering; and fusing the screened features into a joint feature vector, training by using a Cox regression model, and constructing a joint prediction model capable of outputting a risk score and a corresponding risk hierarchical threshold. The invention further relates to an evaluation system based on the model, a storage medium and a kit applying the model. According to the method, the functional serum glycomics characteristics are introduced into prognosis prediction of the specific treatment scene for the first time, the defects that an existing prediction method is single in index and limited in precision are overcome through multi-dimensional information integration, more accurate individualized risk layering can be achieved, a reliable tool is provided for clinical treatment decision making, and the method has important clinical application value.

The invention relates to the technical field of biological information and glycomics detection, and discloses a method and system for calibrating the molecular weight of a carbohydrate chain based on capillary electrophoresis, and the method comprises the following steps: obtaining a multi-stage standard product set; collecting migration time of the to-be-detected carbohydrate chain and the standard substance; introducing an interior label correction factor to obtain correction migration time; constructing a cubic polynomial calibration model based on the multi-stage standard product; a GU interval segmentation optimization strategy is combined; and outputting an optimized molecular weight predicted value. Compared with the prior art that a calibration mode that only a single standard substance is adopted and depends on a linear model is adopted, and particularly under the condition that migration time fluctuation is remarkable or a GU value exceeds a linear coverage range, high-precision calibration cannot be achieved, the method and the device have the advantages that by means of internal standard correction, multi-standard-substance modeling and a dynamic segmentation optimization mechanism, high-precision calibration can be achieved; high-precision calibration of the full-interval sugar chain is realized, and the accuracy of the calibration result is improved.

A sample preparation workflow to facilitate deep N-glycomics analysis of human serum by capillary electrophoresis with laser induced fluorescence (CE-LIF) detection accommodates the higher sample concentration requirement of electrospray ionization mass spectrometry connected to capillary electrophoresis (CE-ESI-MS). A temperature gradient denaturing protocol is applied on amine functionalized magnetic bead partitioned glycoproteins to avoid precipitation. This also results in the free sugar content of the serum being significantly decreased which allows PNGase F mediated release of the N-linked carbohydrates. The liberated oligosaccharides were tagged with aminopyrene-trisulfonate, utilizing a modified evaporative labeling protocol. This workflow provides appropriate amounts of material for example for use in CE-ESI-MS analysis in negative ionization mode.

The application discloses a trap glycan library construction method and system based on topological graph editing and global optimization, and belongs to the technical field of glycan analysis, which comprises the following steps: analyzing original glycan into a topological structure, calculating the number of each preset type of monosaccharide to obtain a monosaccharide composition vector, applying numerical disturbance to the monosaccharide composition vector to obtain a candidate trap monosaccharide composition vector, and then calculating the difference between the monosaccharide composition vector and the candidate trap monosaccharide composition vector, so as to edit the topological structure to obtain an effective candidate trap topological structure; and performing global distribution on the original glycan group and the effective candidate trap glycan group to screen out a unique effective candidate trap glycan group, and combining all the selected effective candidate trap glycan groups to form a trap glycan library. The application aims to solve the problems of unreasonable trap glycan structure, large quality distribution deviation and difficulty in processing isomers in the prior art, so as to improve the accuracy of glycomics identification error rate estimation and quality control.

The invention discloses a comprehensive prediction model for predicting treatment drug resistance of hepatocellular carcinoma patients and a construction method and application thereof. The method comprises the following steps: acquiring clinical index data and serum glycomics characteristic data of a hepatocellular carcinoma patient before treatment; screening out key clinical indexes and key carbohydrate chain features significantly related to drug resistance, and integrating the key clinical indexes and the key carbohydrate chain features into a joint feature vector; and training to obtain a comprehensive prediction model through a logistic regression algorithm by using the combined feature vector and the corresponding drug resistance state tag. The invention also provides a prediction system comprising the model, and a special kit for generating standardized saccharomics characteristic data. Through fusion of macroscopic clinical information and microscopic carbohydrate chain molecular information, the combined prediction model constructed by the invention significantly improves prediction precision, can noninvasively and early evaluate the risk of primary or secondary drug resistance of a patient before treatment, and provides a reliable decision basis for clinically formulating an individualized treatment scheme.

The application belongs to the technical field of multi-agent, and particularly relates to a glycomics agent construction method and system based on three-level coding and a storage medium, which maps sugar chain sequences, molecular graph topologies and three-dimensional space conformations to the same vector space through a three-level coding mapping network to generate multi-modal semantic representation vectors; inputs the representation vectors and multi-source spectral graph data into a multi-modal agent to output predicted sugar chain structures with structural unit confidence through cross-modal fusion and uncertainty quantification; generates research hypotheses automatically based on the prediction results and the confidence on a glycomics knowledge graph through k-hop random walk; generates recommended experimental parameters in an experimental parameter space through Bayesian optimization; issues the recommended experimental parameters to an automated experimental platform and receives results, evaluates deviation through KL divergence calculation, automatically updates knowledge graph weights and fine-tunes the multi-modal agent; and the application breaks the whole unmanned link from original spectral graphs to new knowledge discovery.

The invention discloses a kidney biological age calculation method based on glycosylation markers and integrated machine learning, and belongs to the technical field of bioinformatics and health monitoring. The method comprises the following steps: firstly, acquiring N-glycosylation spectral pattern data of plasma immunoglobulin G of an individual and preprocessing; secondly, carrying out feature dimension reduction on the glycosyl data by adopting a self-adaptive group elastic network, and screening candidate glycosyl structures; then, further screening out a target glycosyl structure associated with kidney functions by using an integrated machine learning algorithm, and performing causal inference through Mendel randomization to determine a final glycosyl structure; and finally, based on the final glycosyl structure and the individual age, constructing a multi-factor Logistic regression model, and calculating to obtain the kidney glycosyl biological age of the individual. According to the method, quantitative evaluation of the kidney specific biological age is realized by integrating glycomics data and a machine learning algorithm.