36 results about "Disease markers" patented technology

The invention discloses a biosensor for detecting Alzheimer's disease markers as well as a construction method and application thereof, and relates to the technical field of biosensors. The construction method comprises the following steps: after a glassy carbon electrode is cleaned, dropwise adding a RuSi NPs (at) CS compound on the surface of the glassy carbon electrode to obtain a RuSi NPs (at) CS / GCE electrode; and immersing the RuSi NPs coated CS / GCE electrode into a PHL: S double-chain solution, and carrying out an incubation reaction to obtain the biosensor. The RuSi NPs (at) CS compound is obtained by carrying out mixed reaction on ruthenium-silicon nanoparticles and chitosan; pHL: S double chains contained in the PHL: S double-chain solution are obtained by carrying out incubation reaction on carboxyl-activated PHL chains and S chains. The biosensor has the advantages of high accuracy, strong sensitivity, good selectivity and the like, and can be applied to accurate detection of the Alzheimer's disease marker A beta oligomer.

A method for judging the structural stability of a disease marker belongs to the field of disease markers, and comprises the following steps: acquiring a disease marker sample, and collecting spectral data, fluorescence intensity data, surface charge data and protein content data of the disease marker as multi-dimensional data of the disease marker; performing spectral stability analysis on the multi-dimensional data of the disease marker to obtain a first judgment result, and if the first judgment result is in a stable state, performing molecular structure scoring analysis on the fluorescence intensity data of the disease marker through a pre-trained deep neural network model to obtain a second judgment result; and if the second judgment result is a stable state, performing structural integrity analysis based on the surface charge data of the disease marker and the protein content data of the disease marker to obtain a third judgment result, and outputting a final structural stability judgment result of the disease marker according to the third judgment result. The technical problem that the overall structure stability of the disease marker is difficult to accurately assess in the prior art can be solved.

The invention provides a disease marker epitope prediction and antibody screening method, which belongs to the technical field of disease markers, and comprises the following steps: firstly, establishing a training data set containing a known antigen-antibody compound structure and disease tissue expression data; and obtaining target protein sequence information through liquid chromatography-mass spectrometry analysis and carrying out sequence comparison. And then a deep convolutional neural network is utilized to extract sequence features, and surface exposure sites are identified by combining secondary structure prediction and solvent accessibility analysis. After the features are integrated with sequence evolution conservative properties, a prediction model is constructed by using a random forest classifier. The method comprises the following steps: carrying out molecular dynamics simulation on a prediction result, screening first 10% of candidate sequences through a comprehensive scoring function and K-means clustering, and finally determining an antigen epitope sequence with the strongest binding activity through verification of an antigen chip and a fluorescence labeled antibody system, so that the technical problem that specific antigen epitopes are difficult to accurately predict and recognize in the prior art is solved.

The invention discloses a multi-nucleic acid probe rapid modification and multi-target detection device based on electric field regulation and control. An instrument main body in the device is integrated with an automatic liquid path system and a micro electrochemical workstation module, and is used for realizing programmable reagent and sample conveying and multi-channel electrochemical measurement. The detachable sensing chip is formed by bonding a microelectrode array chip and a micro-fluidic chip, and is switched through a PCB (Printed Circuit Board) to realize plug and play. According to the present invention, the positive suction-negative discharge strategy of the electric field is utilized, the preferred positive potential is applied to the target electrode to efficiently attract the probe, and the preferred negative potential is applied to the non-target electrode to inhibit the non-specific adsorption so as to achieve the spatial selective rapid fixation and the accurate density regulation and control of the multi-nucleic acid probe in the extremely short time; the device can be used for quantitatively detecting various body fluid disease markers in a plurality of urine samples at the same time in single operation, and an efficient, accurate and portable multi-marker comprehensive analysis solution is provided for early screening and instant diagnosis of tumors.

The present invention relates to the field of data processing technology, and in particular to a genomic data analysis method. The method comprises the following steps: preprocessing genomic data, performing intelligent compression processing on the preprocessed genomic data using an adaptive multidimensional space compression algorithm to obtain compressed genomic data; performing gene variation detection and filtering on the compressed genomic data to obtain variant genomic data, and performing functional annotation to obtain genomic data with annotation results; performing gene expression analysis on the genomic data with annotation results to obtain gene expression data; and performing association analysis based on the genomic data with annotation results and gene expression data using a gene variation-phenotype association analysis method to screen out potential disease markers. The method solves the technical problems of inaccurate genomic data processing, low computational efficiency, and low analysis accuracy in the genomic data analysis process of traditional genomic data storage and processing technologies.

The invention relates to the technical field of image processing, in particular to a retinopathy recognition method, system and device based on artificial intelligence and a medium, and the method comprises the steps: taking the eye of a detected pregnant woman as a target object, setting a preset position, scanning in the depth direction, obtaining point fault information, carrying out three-dimensional reconstruction, and generating a second image. Generating a third image, performing correction processing, presetting a straight line in the corrected image, obtaining pixel values corresponding to the positions where the first pixel and the second pixel are located, and obtaining the thickness data of each layer; taking the thickness data as a pixel value; setting the value of each layer of pixel to be equal to the thickness data of the layer; generating a fourth image; and establishing an image database, adding a disease marker to the reference image, training the image estimation model by using the reference image added with the disease marker, and outputting a disease corresponding to the fourth image. The method improves the prediction efficiency and accuracy of the eye fundus image.

The invention belongs to the technical field of mass spectrum ionization sources, provides a tandem mass spectrum ionization source for online detection of disease markers in tail end gas of expired gas of a human body, and aims to solve the problems of complex expired gas sampling, insufficient detection sensitivity and difficult real-time monitoring in the prior art. According to the ionization source device, real-time online expiration analysis of expired gas of a human body can be achieved, the expired gas end gas moment can be determined according to the change of the concentration of carbon dioxide in the expired gas, and therefore real-time analysis of disease markers in the expired gas end gas is achieved. According to the technical scheme, the design that double ionization sources are connected in series is adopted, and a funnel-shaped chemical ionization source is arranged at the front end and specially used for high-sensitivity detection of volatile organic compounds in exhaled air; the photoelectron ionization source is arranged at the rear end and used for monitoring the carbon dioxide concentration in real time and determining the end gas moment. According to the design, high sensitivity of organic matter detection and accurate monitoring of carbon dioxide concentration are both considered in a partitioned ionization mode. The efficient, sensitive and reliable mass spectrum ionization source device is provided for exhaled gas detection, automatic distinguishing of the front end and the tail end of exhaled gas and high-sensitivity analysis of disease markers in tail end gas can be achieved, the sampling process can be simplified, the detection efficiency can be improved, and the mass spectrum ionization source device has wide application prospects, can be suitable for various scenes and has wide application prospects. The method includes clinical disease early diagnosis, health monitoring and the like.

The present invention provides a light-activated lateral flow immunosensor reagent strip, a detection and analysis method, and its application. The reagent strip comprises an electrode substrate, a conjugate pad, a sample backing layer, a chromatographic membrane, and a water-absorbing pad. The sample backing layer, conjugate pad, chromatographic membrane, and water-absorbing pad are connected end to end and sequentially overlapped and affixed to the electrode substrate. The light-activated lateral flow immunosensor reagent strip is used for the quantitative analysis of various disease markers in biological fluids such as blood, saliva, cerebrospinal fluid, or urine.

The disclosure relates to systems and methods for evaluating markers of disease by using optical techniques A method includes analyzing data from an imaging of an eye of a patient with at least one processor, the data from the imaging including a plurality of pixels and including, for each pixel, spatial, spectral, and polarimetric data generated from the imaging of the eye. The method further includes based on the analyzing, classifying the patient into at least one category of a plurality of categories, each category of the plurality of categories indicating a status with respect to a neurodegenerative disease that affects a central nervous system. The method further includes generating an output of one or more of the at least one category indicating the status of the patient with respect to the neurodegenerative disease that affects a central nervous system.

A method for capturing distinct higher order interactions of a dataset relevant to biological inferences includes deriving Hamiltonian parameters for the dataset, wherein the dataset includes data responsive to a phenotype of interest. The method further includes generating a partition function responsive to the Hamiltonian parameters; calculating cumulant moments of the partition function and deriving higher order cumulants using the Hamiltonian parameters, wherein the higher order cumulants are responsive to the phenotype of interest.

The invention provides a human body expired gas detection method and device based on deep learning and a semiconductor sensor array, and belongs to the technical field of gas detection. The problem that an existing semiconductor gas sensor is low in mixed gas recognition accuracy is solved. Comprising the following steps: acquiring expired gas R-t response curve data of a patient as sample data; constructing mixed gas, and obtaining and storing a mixed gas R-t response curve group; the collected patient exhaled gas R-t response curve data and the mixed gas R-t response curve set are preprocessed, and a patient exhaled gas gas response curve data set is constructed through the preprocessed patient exhaled gas R-t response curve set; constructing an expired gas disease marker fitting data set by utilizing the preprocessed mixed gas R-t response curve group; fitting a data set by using the exhaled air disease markers to construct an evaluation system; constructing a recognition model, and performing learning training on the recognition model by using the data set; the method is applied to human body exhaled air detection.

To provide renal disease markers for accurately evaluating and determining a wide range of renal diseases in subjects.SOLUTION: A method of assisting in evaluation of renal disease is provided, the method involving the use of a ratio of reabsorption and excretion of D-serine and / or D-asparagine in the kidney of a subject as an indicator.SELECTED DRAWING: None

The present invention relates to the technical field of crop disease and insect pest monitoring, and in particular provides an intelligent crop disease and insect pest monitoring system based on image recognition and machine learning. The system comprises: a benchmark model generation subsystem for acquiring a continuous state stream within the crop growth cycle through a multispectral imaging array; a micro-field comparison of the real-time image of the continuous state stream with an ideal morphological benchmark vector, outputting a biological stress difference map; a mutually exclusive association engine subsystem for purifying the pathological response map, inputting it into a pathological feature dissociation network, and outputting a set of environmental immune disease markers; and a pathogen source trajectory deduction subsystem for analyzing the initial infection focus coordinates, combined with a real-time wind direction vector field and a plant density topology map, through a biological diffusion dynamic chain. The present invention solves the problem of high false positive rates in complex farmland scenarios through a dual denoising mechanism of growth rhythm decoupling and environmental interference stripping, ultimately achieving a leap in monitoring dimensions from single-point identification to group prevention and control.

The present invention relates to a green fluorescent nanosphere, a preparation method and uses thereof, comprising: respectively preparing solutions of green fluorescent gold nanoclusters AuNCs, dendritic mesoporous silica nanoparticles DMSN and polyetherimide PEI; adding the PEI solution to the DMSN solution, mixing evenly and carrying out an ultrasonic reaction to obtain a mixed solution of the DMSN solution coated with PEI; centrifuging and redissolving the obtained mixed solution, adding the AuNCs solution, mixing evenly and carrying out an ultrasonic reaction, and obtaining the green fluorescent nanosphere DMSN@AuNCs after multiple centrifugal purifications. The operation of the present invention is simple, the cost is low, the reaction conditions are mild, and it is green and environmentally friendly. The prepared green fluorescent nanosphere DMSN@AuNCs can be used as a fluorescent labeling material in the field of immunological detection; according to the different antibodies and antigens labeled on the DMSN@AuNCs, a variety of fluorescent detection probes can be formed to detect different targets, and it has high popularization and application value in aspects such as high-throughput detection of disease markers.

The present invention relates to a method for analyzing mucin types and glycan structures of O-linked glycopeptides. More specifically, the present invention relates to a method for identifying types of O-linked glycopeptides using a high-resolution mass spectrometer, a method for analyzing glycan binding positions of O-linked glycopeptides, and a computer-readable recording medium recording a program applied to perform the methods. The methods according to the present invention can efficiently and accurately analyze various mucin types and glycan structures of O-linked glycopeptides, and can be usefully employed to easily discover disease markers from samples for prediction or diagnosis or to analyze the structures of O-linked glycopeptides in glycoprotein drugs by using a high-resolution mass spectrometer.

The invention relates to a multi-channel paper-based micro-fluidic chip for sequential detection of disease markers, the multi-channel paper-based micro-fluidic chip sequentially comprises an inner-ring chip, a reagent ring and a peripheral chip from inside to outside, and the reagent ring can rotate independently; a buffer solution storage area and a plurality of buffer solution runners communicating the buffer solution storage area to the outer edge of the inner ring chip are arranged in the inner ring chip; a plurality of reagent runner groups distributed at intervals are arranged in the reagent ring, and each reagent runner group comprises a marking runner, a developing runner and a flushing runner; a plurality of test areas and a waste liquid storage area communicated with the test areas are arranged in the peripheral chip; the buffer solution flow channels, the reagent flow channel groups and the test areas are in one-to-one correspondence. The multichannel paper-based micro-fluidic core designed by the invention is ingenious in internal flow channel layout, and sequential detection of disease markers can be conveniently and quickly completed by rotating the reagent ring.

The invention relates to a method for screening Alzheimer's disease markers by using a bioinformatics technology and application of the Alzheimer's disease markers. Serum microRNA samples of Alzheimer's disease normal subjects (NC), mild cognitive impairment (MCI) and Alzheimer's disease (AD) patients in an NCBI public database are analyzed, and a bioinformatics analysis method is adopted to systematically identify common miRNA which commonly presents differential expression at the early stage and the late stage of the disease, so that target mRNA of the common miRNA is predicted, and a regulatory network is constructed; and finally screening out specific miRNA closely associated with the key mRNA as a disease monitoring marker. The method breaks through the high-cost limitation of traditional dependence on high-throughput sequencing, realizes efficient and reliable screening and identification of disease molecular markers by deeply mining public data resources, provides a new molecular target and detection means for early diagnosis, illness monitoring and intervention treatment of Alzheimer's disease, and has a wide application prospect. The important clinical application value is realized.

The present invention relates to an Alzheimer's disease marker and risk prediction model based on intestinal flora. The Alzheimer's disease marker based on intestinal flora includes intestinal flora, and the intestinal flora includes anaerobic Lawsonia sibiricum, Escherichia coli, Streptococcus salivarius, Asahidiella pseudomallei, Kluyveromyces pedis, intestinal Roseburia, Roseburia faecalis, Roseburia hominis, Firmicutes CAG424 bacteria and Clostridium CAG58 bacteria. The present invention mines Alzheimer's disease markers based on intestinal flora and further constructs an Alzheimer's disease risk prediction model based on intestinal flora, which has the characteristics of high detection accuracy, convenience, speed, safety and non-invasiveness, and has important clinical guidance significance for assisting the diagnosis of Alzheimer's disease-related indicators.

The application discloses a radial multilayer microneedle sensing system and method for single follicular unit microenvironment analysis, and belongs to the field of skin appendage detection and biological micro-electro-mechanical systems. The system comprises a positioning hole integrated with an optical collimation unit, concentric ring microneedles with a length ratio within a certain range, ring compression protruding structures between the microneedles, a differential negative pressure sampling module and a detection electrode modified with an aptamer probe, which can accurately position the follicle, perform layered puncture, construct a hydraulic barrier to prevent cross-flow, isolate and extract interstitial fluid between deep and shallow layers and target detection of disease markers such as androgen alopecia. The method comprises the steps of optical targeting positioning, compression sealing puncture, differential sampling, in-situ detection and gradient index calculation. The application can improve the spatial detection resolution of the markers, solve the problems of signal dilution and subcutaneous cross-flow, and provide a quantitative tool for follicular pathological research and drug non-diagnostic screening.

The present invention discloses a biosensor for detecting Alzheimer's disease markers, as well as a construction method and application thereof, relating to the field of biosensor technology. The construction method comprises the following steps: after cleaning a glassy carbon electrode, dripping a RuSi NPs@CS complex onto the surface of the cleaned glassy carbon electrode to obtain a RuSi NPs@CS / GCE electrode; immersing the RuSi NPs@CS / GCE electrode in a PHL:S double-strand solution for incubation to obtain the biosensor; the RuSi NPs@CS complex is obtained by mixing ruthenium silicon nanoparticles and chitosan; the PHL:S double-strand solution contains PHL chains activated by carboxyl groups and S chains through an incubation reaction. The biosensor has the advantages of high accuracy, strong sensitivity, and good selectivity, and can be used to accurately detect Aβ oligomers, a marker of Alzheimer's disease.

The present application provides a kind of road disease distribution visualization method and system, including data acquisition module through mobile detection vehicle equipped with industrial camera, three-dimensional laser scanner and combined navigation device, real-time collection disease data, and carry out space-time synchronization and pretreatment;Disease analysis module realizes the space alignment of multi-source data based on position data, identifies the disease boundary in image through semantic segmentation and maps to scanning data, extracts disease point set and calculates disease area, depth and volume parameters;Decision module integrates historical and real-time disease parameters, weather data, generates disease evolution trend by combining time series prediction model, and marks high-risk areas by risk index;Visualization module dynamically renders disease markers on three-dimensional map, supports interactive display of historical changes on time axis, and dynamically renders high-risk areas on heat map.The present application realizes multi-dimensional dynamic monitoring and risk warning of road disease, and improves road maintenance decision efficiency.

The application discloses a kit for detecting Alzheimer's disease markers and a preparation method thereof, and relates to the technical field of immune detection. The kit for detecting Alzheimer's disease markers comprises a shell and a test strip. The test strip comprises a bottom plate extending along a first direction, and a sample pad, a combination pad, a coated pad and a water absorption paper which are arranged on the bottom plate and are spaced apart along the first direction. The combination pad is coated with A beta 1-40 labeled first antibody, A beta 1-42 labeled first antibody and quality control labeled first antibody. The coated pad is sequentially provided with a first detection piece, a second detection piece and a quality control piece along the first direction. The first detection piece is coated with one of A beta 1-40 second antibody and A beta 1-42 second antibody. The second detection piece is coated with the other one of A beta 1-40 second antibody and A beta 1-42 second antibody. The quality control piece is coated with quality control second antibody. The technical scheme of the application can realize simultaneous detection of A beta 1-40 and A beta 1-42.

The invention discloses a biosensor based on an extended gate field effect transistor and a preparation method and application thereof.The sensor comprises an FET device, an extended gate electrode and a signal detection system, the metal surface of the extended gate electrode is modified through an L-cysteine self-assembled monomolecular layer, and specific recognition molecules are crosslinked and covalently fixed through EDC / NHS, so that the extended gate field effect transistor is obtained. And the grid voltage measuring module is used for applying grid voltage to the reference electrode and measuring the source-drain current change of the FET device. Through an optimized surface modification strategy, high-density and stable fixation of recognition molecules is realized, the sensor has high sensitivity, high specificity and good stability, the expanded gate electrode can be repeatedly used, the cost is reduced, the sensor can be integrated with a portable detection system, and the sensor is suitable for popularization and application. The kit is used for detecting disease markers in samples such as blood, urine and the like, such as an AQP4 antibody against neuromyelitis optica, and has a wide application prospect in the fields of early diagnosis and instant detection of diseases.

A disease marker functional domain analysis method belongs to the field of disease markers and comprises the following steps: acquiring a primary structure sequence of a to-be-detected disease marker, performing multi-dimensional data decomposition by adopting a multi-sequence comparison method to obtain a conserved sequence fragment and a variable sequence fragment, and calculating the transverse correlation degree and the longitudinal correlation degree of the sequence fragments; constructing a functional domain boundary prediction matrix based on the conserved sequence fragment and the variable sequence fragment, calculating a functional domain boundary stability coefficient, and dividing a functional structure domain; calculating an amino acid conservative score matrix of the functional structure domain, and establishing a functional domain stability discrimination model; evaluating the conservative score matrix by using the functional domain stability discrimination model to obtain a stable to-be-analyzed functional domain; performing evolutionary analysis and experimental verification on the stable to-be-analyzed functional domain, and constructing a functional domain feature vector; the problem that in the prior art, accuracy and reliability of disease marker functional domain analysis are not high enough is solved.

The invention provides a disease marker subtype screening method based on molecular docking, and belongs to the technical field of disease marker subtype screening, and the method comprises the following steps: firstly collecting multi-omics data of a patient and generating an expression profile and a regulation relation matrix, then constructing a molecular hypergraph network model, endowing nodes with features, and constructing hyperedges; and then calculating the marker association degree of hyperedges, extracting hierarchical features by using a hypergraph neural network, and identifying key molecular markers through node importance scores. And then verifying physical interaction by using a molecular docking technology, optimizing a network structure, and retraining the model to obtain a disease marker subtype classification model. And finally, verifying the performance of the model through an independent verification data set, and applying the verified model to disease subtype classification of a new patient. The method provided by the invention solves the problem of lack of verification of molecular physical interaction in the prior art, and more accurately screens out disease markers.

The invention discloses a multi-dimensional efficient DNA sequence pattern mining method, and aims to identify a pattern with biological significance in a DNA sequence. According to the method, firstly, an original database containing DNA sequences and dimension information is divided into a DNA sequence database and a dimension database on the basis of a database analysis technology, and secondly, base combinations with remarkable effectiveness under different conditions are extracted from DNA sequence data through an efficient sequence pattern mining algorithm; and finally, generating a multi-dimensional efficient DNA sequence mode in combination with a multi-dimensional data mining technology. According to the method, experimental measurement values or importance of all basic groups are quantified into utility values, deep mining is carried out on multi-dimensional DNA data, and high-utility gene segments under specific species, tissue types and experimental conditions are identified. According to the method disclosed by the invention, the efficiency and depth of DNA sequence analysis are remarkably improved, and a functional sequence mode which is difficult to find by a traditional method and has condition specificity can be systematically revealed, so that the scientific preciseness and the practical application value of research results are improved in multiple aspects of gene function analysis, disease marker discovery, evolution research and the like.

The invention relates to the technical field of chemical analysis and detection, and discloses a universal double-locking chemiluminescence probe skeleton based on deep learning aided design and a preparation method and application thereof, the chemiluminescence probe skeleton is CBCN, and the structural general formula of the CBCN is shown in the specification; wherein R is selected from one of an enzyme sensitive group, a redox response group, and an analyte responsive group having a boron-containing group of formula-B (Z) (Z ') or-B (Z' ') 3-Kat +. A first lock is unlocked after a framework of the chemiluminescence probe reacts with a response group of an object to be detected, a large number of pre-chemiluminescence groups are generated, then obvious chemiluminescence can be generated after NaClO and H2O2 are added, and the chemiluminescence probe has the advantages of being high in sensitivity and easy to operate when being applied to detection of microorganisms and disease markers.

The invention provides a marker molecule subtype division method based on sequence homology, which belongs to the technical field of disease markers, and comprises the following steps of: performing quality screening and standardization processing on an input sequence by establishing a sequence quality evaluation model of a double-layer game structure; a MinHash algorithm is adopted to calculate approximate similarity between sequences, a similarity matrix is generated to reduce calculation complexity, a hierarchical clustering algorithm is adopted to construct a homology tree structure based on the similarity matrix, and a hierarchy center is determined through a dynamic threshold value. A cross center between different levels is established through a cross validation method, weight distribution is optimized by adopting a sequence subtype adaptability model, a special processing center is established for missing data in the comparison process, and the influence of the processing center on a classification result is adjusted through a weight function. And finally, outputting a subtype classification result based on the sequence subtype adaptive model of the Transform architecture. The technical problem that the subtype division accuracy is reduced due to sequence quality difference is solved.

The application discloses a kind of based on the rapid modification of multi-nucleic acid probe and multi-target detection device of electric field regulation and control.The instrument main body in the device integrates automatic liquid system and miniature electrochemical workstation module, for realizing programmable reagent and sample delivery and multi-channel electrochemical measurement.Detachable sensing chip is bonded by microelectrode array chip and microfluidic chip, and is connected by PCB board, to realize plug and play.The application uses electric field "positive suction-negative discharge" strategy, applies optimal positive potential to target electrode to efficiently attract probe, while applying optimal negative potential to non-target electrode to inhibit non-specific adsorption, so as to realize spatial selective rapid fixation and density accurate regulation of multi-nucleic acid probe in very short time.The device can simultaneously quantitatively detect multiple body fluid disease markers in multiple urine samples in a single operation, providing an efficient, accurate and portable multi-marker comprehensive analysis solution for early screening and instant diagnosis of tumors.

The invention provides a disease marker prediction algorithm based on artificial intelligence and application of the disease marker prediction algorithm in Yinqiao heat-clearing tablets and Jinhong tablets. The method comprises the following steps: firstly, constructing a low-dimensional vector embedding method based on a pre-training model to perform feature intelligent extraction on diseases / phenotypes / syndromes and molecules; according to the invention, a single-classification auto-encoder neural network model is constructed for screening negative samples, and a data set is divided into a pre-training data set and a fine-tuning data set. And designing a disease marker prediction algorithm based on a mutual attention mechanism according to the extracted disease / phenotype / syndrome and molecular characteristics. Finally, the method is adopted to analyze two embodiments of wind-heat and dryness cold intervention of the Yinqiao heat-clearing tablets and liver-stomach disharmony chronic superficial gastritis intervention of the Jinhong tablets. Experimental results show that the method provided by the invention is superior to a baseline method in disease / phenotype and molecular correlation prediction performance, is superior to the baseline method in syndrome and molecular correlation prediction performance, and is superior to two widely used tools. According to the method, development of personalized treatment is promoted, possible marker molecules can be predicted according to individual diseases and syndrome states through prediction of disease and syndrome markers, traditional Chinese medicine diagnosis and treatment are promoted to develop in a more accurate direction, and objective technical support is provided for clinical practice.