379 results about "Precision medicine" patented technology

The present disclosure provides an intelligent decision reasoning method for type-based diagnosis and treatment of a cardiovascular disease, a device, and a product, and relates to the medical field. The present disclosure adopts a risk factor mining model of a cardiovascular disease and a drug attribute association mining model of the cardiovascular disease to extract relevant information from cardiovascular data, achieving precise prediction of a fine-grained cardiovascular disease type and effectively promoting precision medicine. Based on a clinical diagnostic dataset and a clinical symptom diagnosis knowledge system of the cardiovascular disease, a type-based auxiliary diagnosis model of the cardiovascular disease is constructed, and intelligent decision reasoning is performed to obtain an association rule. This achieves efficient integration of type-based decision support of the cardiovascular disease and a medical convalescence knowledge base of the cardiovascular disease, and enhances quality of a cardiovascular medical convalescence association rule set.

The invention discloses a medical adverse event risk prediction, prevention and control method based on causal inference. The method comprises the following steps: S1, collecting and preprocessing multi-modal medical data; s2, constructing a causal network through a time slicing technology; s3, introducing ethical factors to form an ethical causal network; s4, generating a causal enhancement vector; s5, constructing a dynamic risk prediction model, and outputting an evaluation result; s6, analyzing a path contribution degree, and screening high-risk paths and nodes; s7, generating a personalized prevention and control scheme; and S8, generating a transparent report, and displaying the transparent report to the medical personnel and the patient. According to the method, the dynamic causal relationship among the medical variables is accurately modeled through the dynamic causal network, the multi-modal data fusion and the ethical correction technology, the personalized prevention and control scheme is generated in combination with the optimization algorithm, comprehensive support is provided for medical decision making, and implementation of precise medical treatment is promoted.

The invention relates to the technical field of vascular surgery, in particular to a vascular surgery interventional intracavity treatment postoperative complication prediction and management system which integrates data acquisition, processing, model training, prediction analysis, comprehensive decision and early warning management and is specially designed for vascular surgery interventional intracavity treatment. The system monitors the postoperative physiological indexes of a patient in real time through multiple sensors, combines clinical data and disease history, carries out physiological sequence prediction and image analysis by utilizing RNN and CNN models, and accurately evaluates the risk of complications. The comprehensive decision-making module generates a personalized management scheme, and the early warning management module monitors in real time and pushes early warning and suggestions in time when abnormity occurs. The system not only improves the prediction accuracy and the management effect, but also optimizes the medical resource configuration, enhances the patient experience, contributes to the development of precise medical treatment and intelligent management, and effectively solves the key problem of postoperative complication prediction and management.

A spatial omics multi-modal fusion method under a single cell level comprises the following steps: extracting spatial morphological characteristics of differential expression genes and cell nucleuses from spatial transcriptome data, single cell sequencing data and histological images, and realizing field adaptation among different platforms by using a conditional variation auto-encoder. And based on a probability inference model, fusing spatial transcriptome expression, unicellular omics and morphological characteristics, and jointly inferring the type and gene expression level of each cell. A spatial cell network is constructed through a graph attention mechanism, and spatial diffusion and recognition of cell types in a full slice range are realized. In combination with a multi-omics enhancement module, undetected gene and protein expression is completed based on expression similarity, and prediction consistency is improved through spatial correction. According to the method, high-resolution reconstruction of single-cell multi-omics information in a three-dimensional space is realized, the information coverage and spatial resolution of spatial omics data are improved, and an efficient and low-cost solution is provided for spatial biology and precise medical research.

The invention relates to the technical field of clinical experiment data processing, and discloses a clinical experiment data analysis method based on reinforcement learning, and the method comprises the steps: obtaining time sequence physiological parameters and treatment scheme execution state parameters; performing hierarchical feature extraction on the time sequence physiological parameters, constructing a dynamic response feature model, and generating an initial treatment response evaluation model in combination with the execution state parameters; loading risk constraints and curative effect indexes to generate a comprehensive evaluation model, and simulating to obtain strategy regulation and control data; constructing and training a reinforcement learning decision model, and generating a treatment path correction model and initial treatment parameters; obtaining dynamic strategy adjustment information, and constructing a multi-stage optimization model for iterative optimization to obtain optimal treatment path parameters; reasoning a curative effect standard reaching probability based on a real-time model, and adjusting a strategy to realize dynamic curative effect matching. According to the method, the clinical data dynamic analysis and treatment scheme optimization capability is improved, and the method is suitable for precise medical scenes.

The invention belongs to the technical field of medical image processing, and discloses a wound identification and charging system based on precision medical treatment, which realizes intelligent management of wound treatment through cooperative work of multiple modules. The method comprises the following steps: firstly, collecting a multi-angle digital image of a wound surface, performing standardization processing, performing image enhancement and noise reduction, and then performing identification and segmentation by using a deep learning algorithm; and performing three-dimensional reconstruction based on the accurate contour map to form parameter feature vectors, and performing intelligent wound type discrimination and severity evaluation. And according to an evaluation result, matching an optimal dressing change type and predicting required consumables, generating a precise dressing change scheme, and further calculating personalized charges and performing transparency verification. And finally, the system automatically generates a standardized medical service file through historical data comparison and rationality analysis. According to the system, the precision, standardization and transparency of wound treatment are realized, the medical service quality is improved, and the resource allocation is optimized.

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 present invention belongs to the technical field of bioengineering. Provided are an IscB mutein and the use thereof. Compared to the wild-type IscB protein, the IscB mutein comprises arginine at at least one of the following amino acid positions: position 84, position 96, position 102, position 111, position 159, position 368 and position 386; preferably, the IscB mutein further comprises arginine at position 401 and position 456. The relatively strong interaction between the IscB mutein and a double-stranded DNA target improves the editing activity for mammalian cell lines, which can greatly enhance the application potential of the IscB mutein as a new-generation underlying tool for gene editing, and promote the use of the IscB mutein in the aspects of precision medicine, animal model construction and crop breeding.

The invention discloses an automatic medicine vending machine based on AI inquiry. The automatic medicine vending machine comprises a machine body, an intelligent interaction system, an AI inquiry system, a medicine storage bin, a control system, a payment system and a printing device. A constant-temperature and constant-humidity system and a disinfection and air purification module are arranged in the machine body. The intelligent interaction system collects symptom information in a multi-mode mode, and the AI inquiry system achieves symptom analysis, intelligent triage and personalized medication recommendation based on a medical knowledge graph and a natural language processing technology. The medicine storage bin carries a three-dimensional matrix type medicine cabinet and an intelligent inventory system, and intelligent replenishment is carried out through an LSTM algorithm. And the control system adopts a controller and triple verification to realize high-precision medicine discharging operation. The payment system supports multiple settlement modes and block chain evidence storage data. And the printing device outputs the medication guide sheet and the anti-counterfeit label. According to the invention, AI and automation technologies are deeply integrated, the service of less than or equal to 3 minutes in the whole process of inquiry-payment-medicine dispensing is realized, and the safety and convenience of medicine use are improved.

The invention provides an asymmetric drug interaction prediction method based on a diffusion diagram attention network, and relates to the technical field of drug interaction prediction.The method comprises the steps that a directed graph network is constructed by using chemical structure features of drug molecules, and features are extracted from drug applying and receiving perspectives through a two-way diagram attention network; therefore, the asymmetry between the drugs is effectively represented. Furthermore, a diffusion model is introduced to carry out noise injection and de-noising processing on a graph structure, so that the adaptability of the model to sparse data and the robustness of feature extraction are remarkably enhanced. Finally, multi-modal features are fused through a deep neural network, and high-precision prediction of drug interaction is realized. Compared with the prior art, the method has the advantages that the prediction accuracy exceeds that of the prior art, the dependence on labeled data is greatly reduced, the feasibility and generalization ability of the model in practical application are greatly improved, and a new technical path is provided for safety evaluation and precise medical research of drug combination therapy.

The invention discloses a tumor personalized drug recommendation method and system based on fusion of multiple clinical guidelines, and belongs to the technical field of bioinformatics and precision medicine. The method comprises the following steps: acquiring gene variation data and clinical feature information of a patient; generating a preliminary drug candidate list based on drug recommendation rules of a plurality of clinical guidelines; calculating the weight of each guide by adopting a dynamic weight distribution algorithm; obtaining drug-gene-disease associated information through multi-hop reasoning of the knowledge graph; calculating a drug evidence score by adopting a multi-dimensional scoring algorithm; carrying out personalized score adjustment in combination with individual features of the patient; and outputting a personalized drug recommendation result. According to the method, through multi-guide dynamic fusion, multi-dimensional evidence scoring, knowledge graph reasoning and personalized adjustment, the problems of incomplete guide coverage, lack of personalization, low response speed and the like in the prior art are solved, the method has the advantages of high accuracy, high clinical applicability, quick response and the like, and the clinical real-time decision-making requirement can be met.

The invention provides a gestational diabetes risk prediction system and method based on multi-modal data fusion, and the system comprises a data collection module which is used for integrating clinical indexes and medical record text data; the data preprocessing module converts the multimode data into numerical values and text variables which can be used for modeling; the variable screening module is used for extracting data features by adopting LASSO regression in combination with a recursive feature elimination algorithm and a Clinical-BERT model; the data prediction module is used for constructing a GDM risk prediction model through a dual-channel calculation unit and a fusion unit, generating an accurate risk probability and providing an interpretable clinical index in combination with an SHAP value; and a prediction result is output through the output module in the forms of a dynamic column diagram, a webpage calculator and an API interface, so that clinical operation and application are facilitated. According to the method, the limitation of a traditional prediction method is broken through, the prediction precision and the real-time monitoring capability are improved, and the development of precise medical treatment is promoted.

The invention belongs to the technical field of gene editing, and discloses a gene editing system and application. The invention provides a gene editing system. The gene editing system comprises Cas9 nickase, sgRNA (small guide ribonucleic acid) and annular petRNA. According to the present invention, the target chain cutting mediated reverse pilot editing (Split Reverse Prime Editing, srPE) realizes the precise editing of the non-target chain cutting site upstream sequence, such that the gene editing coverage range is expanded from the specific region only limited to the non-target chain cutting site downstream to the whole genome range, the stable and efficient editing ability is maintained in different cell types, and the target chain cutting mediated reverse pilot editing can be provided for the target chain cutting site. And meanwhile, the off-target risk is reduced. The srPE gene editing technology breaks through the fundamental limitation of the prior art, and provides a more comprehensive and more efficient technical tool for precise medicine and gene therapy.

The invention belongs to the technical field of organoid chips of biological medicine, and particularly relates to a multi-organoid chip and a preparation method thereof. The multi-class organ chip comprises an upper-layer chip and a lower-layer chip which are detachably arranged; 2-10 organ-like chambers are embedded in the upper-layer chip, and 2-5 porous membranes are arranged at the bottoms of the organ-like chambers; the lower-layer chip is provided with a flow channel, and the flow channel is communicated with the organ-like chamber. Compared with the existing multi-class organ chip technology, the multi-barrier construction in the independent cavity is realized, and meanwhile, the multi-class organ chip is combined with a transwell component for use, so that the personalized combination of the multi-class organ chips is realized. The method is economical and fast, can play an important role in promoting the fields of precise medicine, medicine evaluation, disease models, especially in-vitro evaluation and simulation of traditional Chinese medicine and the like, and is suitable for popularization and application.

The invention relates to an extracellular vesicle surface protein detection paper-based kit and application thereof, and belongs to the technical field of biology. The technical problems that an existing sEVs detection technology is insufficient in specificity, depends on professional instruments and cannot meet the instant detection requirement are solved. According to the method, sEVs are synergistically captured through double aptamers, the target specificity is improved, signals are amplified in a cascaded mode through hyper-branched rolling circle amplification (HRCA) and a CRISPR / Cas12a system, and visual detection is achieved by detecting FAM-biotin probe separation before and after cutting through a transverse flow test strip. According to the method, interference of free protein is effectively avoided through double-target recognition, and the detection limit is as low as 406 parts / mL; cRISPR and a paper-based technology are combined to realize high sensitivity and instant detection; the system is simple and convenient to operate, does not need professional instruments, is suitable for tumor diagnosis and treatment monitoring of clinical samples, and provides a portable solution for precise medical treatment.

The invention provides a method based on Pearson's collinearity selection (PCS). The method is used for screening features closely related to a target phenotype from high-dimensional genome data. According to the PCS method, by calculating Pearson's correlation coefficients among the features, redundant features are effectively removed, the multi-collinearity problem is reduced, the quality of feature selection is remarkably improved, and the stability of model prediction is enhanced. The method is particularly suitable for the fields of genotype and phenotype correlation analysis and the like, and can optimize the data processing and analysis process. In addition, the PCS method has high cross-population adaptability. Feature selection and prediction verification are carried out on data sets of multiple populations, it is proved that multiple models can keep consistent good prediction performance among different data sets by using the PCS method, and the excellent generalization ability is shown. The method not only can effectively predict the phenotype of a single population, but also can be successfully applied to prediction tasks of other populations, and the application range of the method in the fields of precision medicine, crop breeding and the like is further widened. Compared with a traditional feature selection method, the PCS method has the advantages that feature redundancy is reduced, meanwhile, features highly related to the target phenotype can be better reserved, the prediction precision of the model is remarkably improved, and more efficient and reliable technical support is provided for genome data analysis.

An ensemble of predictive models that elucidate how cancer mutations impact the response to common replication stress-inducing (RSi) agents. The models implement recent advances in deep learning to facilitate multi-drug prediction and mechanistic interpretation. Initial studies in tumor cells identify 41 molecular assemblies that integrate alterations in hundreds of genes for accurate drug response prediction. These cover roles in transcription, repair, cell-cycle checkpoints, and growth signaling, of which 30 are shown by loss-of-function genetic screens to regulate drug sensitivity or replication restart. The model translates to cisplatin-treated cervical cancer patients, highlighting an RTK (receptor tyrosine kinase)-JAK-STAT assembly governing resistance. This invention defines a compendium of mechanisms by which mutations affect therapeutic responses, with implications for precision medicine.

The invention provides a cross-omics sparse feature selection system and method based on hierarchical causal modeling, and the system comprises a data input and preprocessing module which is used for receiving multi-omics original data of a multivariate sample; the hierarchical causal structure learning module is connected with the data input and adaptive preprocessing module and is used for constructing a cross-omics hierarchical causal topology; the causal-oriented sparse feature selection module is connected with the hierarchical causal structure learning module; and the model retraining and integration module is used for constructing a three-layer weighted integration discrimination model based on the screened markers, optimizing the fusion weight of each layer through a gradient descent algorithm, and outputting a final prediction result. According to the method, the protein-metabolism biological hierarchy relationship and serum-urine complementary information are fully utilized, and the method has good generalization ability and can be widely applied to marker mining and prediction modeling of cancers, metabolic diseases and the like, so that the accuracy and reliability of precise medical treatment are improved.

The present invention relates to a use of THBS1 as a novel combination drug target that can overcome drug resistance of a targeted anticancer agent. A THBS1 inhibitor according to the present invention inhibits the drug resistance of a target anticancer agent and thus increases an anticancer effect when administered in combination with a target anticancer agent. Accordingly, the present invention can overcome resistance to a targeted anticancer agent and increase a treatment success rate of anticancer drugs for cancer patients, thereby suggesting new possibilities for treatment strategies using targeted anticancer drugs and contributing to the realization of precision medicine.

The invention discloses a patient follow-up visit system and method based on a precise medical image examination item result, and the system comprises a medical image result analysis module which is used for connecting an HIS and a PACS system, carrying out the standardization processing of an image and clinical data through a deep learning model, automatically analyzing a medical image examination report, configuring a differentiated follow-up visit rule through a department rule base, and carrying out the follow-up visit of a patient. Whether follow-up visit is needed or not is judged according to follow-up visit standards set by all departments; the patient screening and task distribution module is used for carrying out priority classification on the patients meeting follow-up visit standards; the follow-up visit execution module is used for contacting the patient, recording follow-up visit feedback and adjusting a follow-up visit plan according to the feedback condition; the return visit effect evaluation module is used for calculating a return visit effect ratio, generating a visual report, quantifying a follow-up visit effect and providing data support for a hospital; and the data analysis and optimization module is used for optimizing the follow-up visit process based on the follow-up visit data. The follow-up efficiency can be improved, the medical resource configuration is optimized, and the follow-up quality of the patient is improved.

The invention relates to the technical field of precision medical treatment, in particular to an interpretation system and method for tumor precision medication and a storage medium, and the system comprises a data collection module which is used for collecting gene data, clinical data and drug response data of tumor patients from a plurality of data sources; the data standardization processing module is used for carrying out formatting and standardization processing on the data, so that the data formats are consistent, and effective processing and analysis can be carried out in subsequent modules; the gene data analysis module is used for performing variation analysis according to the gene data of the patient, identifying a gene mutation site and generating a drug sensitivity analysis result; and the drug screening module is used for screening drugs matched with the gene variation sites of the patient from a drug database according to the gene data analysis result, and providing drug dosage recommendation. By adopting the technical scheme of combining a quantum optimization technology and gene data analysis, the technical effect of remarkably improving the drug screening and dose recommendation precision is achieved.

The invention provides a preparation method and application of a CD70 specific nano antibody molecular imaging probe. The probe comprises a tumor targeting group, radionuclide and a bifunctional chelating agent, the tumor targeting group is a CD70 specific nano antibody; the CD70 specific nano antibody is R8B4, and the amino acid sequence of the R8B4 is as shown in SEQ ID No. 1. The probe provided by the invention can realize noninvasive diagnosis of solid tumors such as renal clear cell carcinoma and malignant tumors of a blood system; reabsorption of the compound in the kidney is effectively inhibited through charge adjustment, kidney uptake is remarkably reduced, non-specific signals are reduced, the signal-to-noise ratio during targeted imaging or treatment is increased, and the imaging quality and treatment safety are improved. The probe not only has a wide application prospect in tumor diagnosis, but also provides a new technical support for targeted therapy and individualized precision medical treatment, and has important scientific research and clinical application values.

The invention relates to the technical field of medical image analysis, and discloses a breast cancer treatment response prediction system based on multi-time-sequence images. According to the method, preoperative MRI, clinical pathology data and RNA-seq data meeting quality control standards are obtained through dynamic case screening, traditional image features, multi-scale Hessian matrix blood vessel-structure features and topological persistent coherent curvature features are extracted through multi-plane segmentation, and key features are obtained through three-level screening. A multi-factor logistic model fusing radiomics, clinical features and immune scoring is further constructed, a pCR probability value and a feature contribution heat map are output, and MRI data quality control, real-time analysis and interactive report generation are achieved through a matched computer system. The method breaks through the limitation of single modal analysis, dynamically associates the image features with the molecular mechanism, and provides interpretable decision support for precision medical treatment.

The invention provides a sleep staging method based on spatial-temporal feature coding and multi-source fusion, and belongs to the technical field of big data analysis. According to the method, collected multi-channel multi-source physiological data including electroencephalogram, myoelectricity and electro-oculogram signals are utilized to construct a sleep staging neural network model fusing the multi-channel multi-source physiological data; establishing a graph space encoder for modeling multi-channel spatial features, and encoding interaction and position information among different channels; establishing a multi-signal source fusion module for fusing multi-source signals, and identifying different contributions of multi-signal sources to stages based on an attention mechanism; a time sequence Transform encoder used for modeling time features is established, and local and global time features are encoded at the same time. According to the method, automatic staging of sleep can be realized, the staging accuracy is improved, personal help can be provided for individuals, a convenient tool is provided for doctors, and heuristic auxiliary guidance is provided for precision medical treatment.

The invention discloses a preparation method and application of a granzyme B response near-infrared two-region ratiometric fluorescent probe (DCGA). The DCGA combines the NIR-II dual-emission fluorescence characteristic of rare earth ions with a ratio-type signal response mode, the sensitivity and signal stability of in-vivo fluorescence imaging are greatly improved, ultralow or enhanced F1060nm fluorescence signals are respectively shown before and after interaction with granzyme B (GzmB), F1525nm fluorescence signals are almost kept unchanged, and the DCGA can be used for detecting the in-vivo fluorescence imaging of the granzyme B (GzmB). Therefore, the self-calibration ratio fluorescence imaging GzmB activity change is realized. The DCGA is applied to tumor treatment of immunotherapy drugs, and responders and non-responders of immunotherapy can be distinguished in a non-invasive early stage through ratio fluorescence signals of the DCGA. Therefore, the fluorescent probe can be used as an effective GzmB active NIR-II ratio fluorescence imaging tool, and is expected to provide powerful support for immunotherapy research, precision medicine and personalized diagnosis and treatment.

The invention discloses a multi-omics cancer subtype recognition method, system and device based on density sensing cluster structure guide contrast learning and a medium, and belongs to the technical field of bioinformatics and artificial intelligence crossing. The method comprises the following steps: acquiring and preprocessing multi-omics data; constructing an omics specific auto-encoder and learning potential representation; constructing a density sensing cluster block in the potential space; constructing a cross-omics positive and negative sample pair based on cluster block sample overlapping; difficult negative sample mining; constructing a cluster block level cross-omics contrast learning target, and training and updating; a self-supervised soft refinement mechanism is introduced to dynamically enhance a cluster structure; and carrying out multi-loss joint optimization and model iteration training. According to the method, the robustness and the stability of a cancer subtype recognition result can be improved, high-dimensional, multi-source and multi-noise multi-omics data can be efficiently modeled and analyzed, good generalization ability and application potential are achieved, and reliable technical support can be provided for cancer subtype research, patient stratified analysis and precise medical aid decision making.

The invention discloses a multi-omics data integration and classification method, system and device based on hierarchical attention, and is applied to the field of precise medical big data analysis. The method comprises the following steps: firstly, generating feature embedding and feature importance scores through a plurality of parallel feature-level attention modules; then, embedding and inputting all the characteristics of the omics into a unified omics-level attention module, and generating omics embedding and omics importance scores; and finally, a classification prediction task is executed based on omics embedding, and a classification result is output for disease classification. The invention completely abandons a traditional dependency graph convolutional network and an integration normal form of variants of the dependency graph convolutional network, and provides a universal hierarchical attention integration architecture. The framework supports classification tasks of any complex diseases, is not limited by omics data types and combination modes, not only is remarkably superior to a traditional integration normal form in classification performance, but also shows a unique negative generalization distance, and proves that the framework has excellent generalization ability. Meanwhile, features and omics importance scores automatically output by the model provide a powerful analysis tool for biomarker discovery and precise diagnosis and treatment of complex diseases.

A method for sharing medical data by creating a Medical Data Ocean (MDO) and a system for maintaining and using the MDO may include receiving medical data from disparate sources; performing datum distillation on the received medical data and integrating the distilled medical data to form integrated and distilled medical data; storing the integrated and distilled medical data into a medical database; receiving a request for information from the large-scale analytics of the integrated and distilled medical data; analyzing the integrated and distilled medical data to identify biological predictors and guide precision medicine; using the analysis of the integrated and distilled medical data to develop personalized treatment plans, predictive models, and / or providing a response to the request for information; and tracking the source of individual medical data from the integrated and distilled medical data used to determine the response to the requested information and / or to develop personalized treatment plans.

The invention provides a method and system for analyzing lung cancer brain metastasis organoid cell subpopulation and functional characteristics, and the method comprises a series of steps: data quality control, batch effect removal, cell grouping, copy number variation inference, quasi-time sequence analysis, function enrichment analysis, and drug sensitivity analysis before and after radiotherapy. Single cell transcriptome sequencing data obtained after organoid culture is subjected to personalized analysis, heterogeneity of tumor cells and sensitivity of the tumor cells to drugs after radiotherapy are described, and the characteristics of all cell subgroups in the lung cancer brain metastasis organoid and the effect of the cell subgroups in the tumor microenvironment can be accurately analyzed; powerful support is provided for mechanism research and precise medicine of lung cancer brain metastasis.

The invention discloses a unified coding, storing and calculating method for large-scale genetic variation data. The method comprises the following steps: a genotype coding and memory representation method: coding a genotype from a | b into a unique integer value; the floating length integer coding method is a method for coding an integer from a traditional fixed length byte to a floating byte and is used for recording an index of a sample; storage coding of a genotype sequence: converting the genotype from an integer value sequence into a byte coding sequence which can be directly stored by a computer; a dynamic translation framework of the genotype sequence: quickly loading the byte coding sequence stored in the hard disk in the step (3) into a memory in a low-load manner; and calculating and coding a genotype sequence: converting the genotype from an integer value sequence into 64-bit space coding accelerated calculation according to allele index (x)-ploidy index (y)-sample group (z). The method is of great significance in improving the data processing efficiency and promoting the development of large-queue genetics research and precise medicine.