50 results about "Polygenic risk score" patented technology

The invention relates to a coronary artery risk prediction method and system for a coronary heart disease patient, and aims to solve the limitation that a traditional static evaluation model is difficult to integrate multi-source dynamic data. Collecting multi-source heterogeneous data of a patient from a hospital information system, a gene database and wearable equipment; the method comprises the following steps: respectively extracting key features through a special processing module: analyzing unstructured text and image plaque features by adopting a deep learning model, calculating a multi-gene risk score, capturing a physiological parameter attenuation trend by utilizing time sequence analysis, constructing a three-stage cascade integration model, screening high-contribution features at the first stage, and constructing a three-stage cascade integration model; the secondary stage integrates space-time dynamic characteristics through an attention mechanism network, the final stage integrates static and dynamic factors to generate a dynamic risk score, a visual risk trend chart and a high-risk factor thermodynamic diagram are output, and real-time early warning and linkage are performed through a medical terminal to generate a personalized intervention scheme. According to the method, collaborative analysis and dynamic risk tracking of multi-dimensional data are realized, and the early warning capability and the clinical decision-making efficiency are remarkably improved.

The invention discloses a medical image automatic identification system based on a neural network, and relates to the technical field of medical image identification. The method is used for solving the problem that early recognition of neurodegenerative diseases is difficult due to medical image and genome data splitting and poor model interpretability in the prior art. The method comprises the following steps: firstly, extracting multi-scale features of a brain structure through a three-dimensional convolutional neural network and a self-attention mechanism, calculating a multi-gene risk score based on a risk site, and encoding the score into a feature vector; secondly, using a cross attention mechanism to take gene features as query vectors, fusing the gene features with image features, and generating brain structure anomaly features under gene regulation; then, gradient weighting class activation mapping is applied to generate a visual thermodynamic diagram, and gene-image association weight weighting is combined to construct a brain region risk distribution diagram; and finally, a high-risk brain region space coordinate set is extracted through threshold segmentation, and an accurate quantification basis is provided for early recognition.

A polygenic risk score (PRS) for coronary artery disease, a construction method therefor, and an application thereof in combination with clinical risk assessment. The present invention first provides an application of a reagent for detecting individual information in preparation of a detection device for assessing the onset risk of the coronary artery disease, wherein the individual information comprises 311 CAD-related single nucleotide polymorphic sites, and the individual information preferably also comprises one or more of BP, BMI, DM, TC and Stroke-related single nucleotide polymorphic sites. The present invention further provides a method for constructing a comprehensive metaPRS for the coronary artery disease. In the present invention, the PRS and the conventional clinical risk factor score are further integrated, such that re-layering of the onset risk of the coronary artery disease can be realized. The present invention is of great significance to primary prevention of the coronary artery disease.

The disclosed embodiments concern methods, apparatus, systems, and computer program products for developing polygenic risk score (PRS) models. In some implementations, a fully automated process is provided that allows for a PRS model to be defined by an initial set of parameters. In some implementations the PRS models are trained to provide a PRS for particular populations.

The invention provides an atrial fibrillation prediction method based on deep learning and polygene scoring, which combines electrocardiogram (ECG) signals, clinical feature data and polygene risk scoring (PRS) based on multi-modal data fusion. By integrating the deep learning model, clinical risk score and polygene risk score, the accuracy and stability of AF prediction are significantly improved. The technical value is embodied in that: multi-source data fusion: combining ECG signals, clinical data and gene information in a single system for the first time, and providing comprehensive individualized risk assessment; real-time analysis capability: based on ECG real-time data processing capability, the real-time analysis capability can be used for dynamic risk assessment; and clinical decision support: a scientific and quantitative AF prediction report is provided for medical staff, and formulation of an intervention strategy is assisted.

The invention relates to the technical field of disease risk prediction and precision medical treatment, in particular to an establishment method and application of a late-onset psoriasis risk prediction model, and the method comprises the following steps: (1) collecting lifestyle data, serum metabolite data, clinical characteristics and polygene risk scores of a subject; (2) constructing a healthy lifestyle score according to the lifestyle data; (3) screening metabolites significantly related to the healthy lifestyle by using a multiple linear regression model; (4) screening metabolites significantly related to the risk of late psoriasis by using a Cox regression model; (5) screening a key metabolite set by adopting an elastic network regression model; and (6) inputting the sample features into a machine learning model to obtain a late-onset psoriasis risk prediction model. According to the method, the prediction accuracy is high, the AUC can reach 0.86 by combining the lifestyle, serum metabolites and genetic risks, and the method is obviously superior to a prediction method only depending on clinical characteristics or genetic information.

The invention provides a noise-induced hearing loss genetic risk prediction method and system, and relates to the technical field of gene detection.The method comprises the steps that a gene Panel is constructed based on noise-induced hearing loss genome data, a sample is sequenced through the gene Panel, and a sequencing result is obtained; performing linkage imbalance pruning on a sequencing result to obtain SNPs (Single Nucleotide Polymorphisms); carrying out dimensionality reduction on the SNPs based on principal component analysis to obtain a plurality of principal component data; and calculating to obtain a polygene risk score, synchronizing the polygene risk score and the plurality of principal component data to the noise-induced hearing loss prediction model for analysis, and determining a genetic risk prediction report. The technical problem that in the prior art, due to the fact that genetic risk prediction research only pays attention to a small number of genes and the information coverage degree is low, the accuracy and applicability of the prediction result are insufficient is solved, the prediction accuracy, stability and generalization ability are improved, and therefore the technical effect of individualized noise-induced hearing loss risk assessment is achieved.

According to one embodiment of the present disclosure, a method for estimating a polygenic risk score (PLS) includes the steps of obtaining data related to a genome-wide association study (GWAS) of an individual for whom the polygenic risk score is to be estimated, obtaining a plurality of primary estimates of the individual's polygenic risk score from the data via a plurality of deep learning models, generating a second test set for a metalearning model using the plurality of primary estimates, and obtaining a final estimate of the polygenic risk score from the second test set via the metalearning model.

An apparatus for generating personalized risk assessments for neurodegenerative diseases includes a computing device that receives user data containing genetic and medical information. It processes the data to create genotype identification and gene detection modules, identifying user genotypes and relevant genetic markers. The user's mitochondrial haplogroup is examined to refine the assessment. A risk calculation module employs machine learning to weigh genetic variants against population-based data, calculating a polygenic risk score (PRS). The PRS forms a personalized risk profile, displayed through a visual interface. The disclosed systems offer a comprehensive approach to accurate risk assessment, enabling targeted interventions and informed decision-making in neurodegenerative disease management.

A hypercholesterolemic profile of a patient is determined on the basis of (i) a first statistical distribution based on a scale of polygenic risk score levels and a scale of cholesterol concentration levels in low-density lipoproteins, the first statistical distribution giving, based on these scales, a distribution of individuals in a first sub-sample of severely hypercholesterolemic individuals having at least one genetic mutation causing hypercholesterolemia for at least one gene from a set of genes and (ii) a second statistical distribution based on the same scale of polygenic risk score levels and the same scale of cholesterol concentration levels in low-density lipoproteins,the second statistical distribution giving, as a function of these scales, a distribution of individuals from a second sub-sample of severely hypercholesterolemic individuals not presenting a genetic mutation in the set of genes;

Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and / or the like for performing predictive structural analysis. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive structural analysis using at least one of techniques using time bound code transition likelihood data objects, techniques using cross-code relationship values, techniques using augmented entity-code occurrence data objects, techniques using per-pathway text representations of inferred occurrence pathways of a one or more individual historic code occurrences, techniques using polygenic risk score (PRS) measures, and / or the like.

This invention discloses a method for calculating a polygenic risk score (PRS or PRS score) for predicting autism and neurodevelopmental disorders (hereinafter collectively referred to as diseases). Its key feature is the improvement in the accuracy of predicting the relative risk of potential diseases by incorporating the genetic effects of rare variants (MAF < 0.1%) and common variants (MAF > 0.1%). This invention uses loss-of-function (LoF) rare variants detected by DNA sequencing (including but not limited to NGS / 3GS) to calculate gene-based PRS scores; and integrates these scores into existing snp-based PRS scores based solely on common variants detected by GWAS; thereby solving the problem of low variance interpretation (pseudo-R) in snp-based PRS scores calculated only based on common variants. 2 (and the problem of low predictive ability.)

The application discloses a kind of Chinese lung cancer high-risk population risk prediction method based on multi-modal feature fusion, comprising: step 1, data acquisition;Step 2, data preprocessing;Step 3, multi-source risk feature evaluation and screening: verify and evaluate the prediction efficiency of lung cancer epidemiology risk score, tumor marker and lung cancer related multi-gene risk score, and screen out the optimal single dimension score of each dimension from it;Step 4, multi-modal score construction: according to the smoking status of participants, the population is divided, the optimal single dimension score of each dimension screened out is standardized, then the multi-source features are fused using Logistic regression model and the weighted coefficients are optimized, and the multi-modal lung cancer risk prediction score suitable for smokers and non-smokers is constructed respectively.The present application has superior discrimination performance, can accurately identify high-risk individuals, guide subsequent imaging examination and early intervention, and improve the early diagnosis rate and treatment effect of lung cancer.

A computer-implemented method for calculating an individual's tailored Polygenic Risk Score is based on known genetic information. A dataset is provided related to a reference panel including genetically characterized individuals with known disease status and diversified global ancestry. An individual-specific genetic reference group of individuals is selected as a subset from the reference panel. Genetic distances of the individual from each of the reference panel individuals are computed; each being the individual's genetic distance from a respective reference panel individual. Individuals of the individual-specific genetic reference group based on the individual's computed genetic distances are selected. The individual's basic Polygenic Risk Score for a disease is calculated to provide the individual's disease risk prediction. An ancestry-based background PRS contribution is determined. The ancestry contribution is removed from the individual's calculated basic Polygenic Risk Score to obtain the individual's tailored Polygenic Risk Score and provide a disease risk prediction.

One embodiment of the present disclosure provides a method for visualizing polygenic risk scores. The method comprises the steps of: checking whether input genotype data matches pre-stored genetic variant risk data, and mapping genes and genetic variants on the basis of the genotype data; calculating, on the basis of a plurality of preset algorithms, polygenic risk scores and attribute values for the gene and genetic variant contribution scores for the genetic variants thereof on the basis of a plurality of preset algorithms; and visualizing and providing, on the basis of the attribute values and the genetic variant contribution scores, gene-specific gene contribution scores at the population level, gene-specific attribute values at the individual level, and gene-specific genetic variant contribution scores at the individual level.

The invention discloses a colorectal cancer related genetic molecular marker with an interaction effect with drinking and application of the colorectal cancer related genetic molecular marker. The genetic molecular marker is a combination of rs61355123, rs17624213, rs3791337, rs190489984 and rs2290476. According to the method, the limitation that a traditional genetic model only pays attention to the genetic main effect is broken through, and the multi-gene risk scoring model integrating environmental factors is constructed by recognizing a group of key SNP loci having remarkable interaction with drinking behaviors. The gene-environment collaborative assessment mechanism realizes more accurate risk stratification for drinkers. Clinical test data show that compared with a traditional genetic model, the genetic score of the integrated genetic marker provided by the invention has the following advantages: (1) the prediction accuracy is improved; (2) the clinical practical value is prominent; and (3) the intervention pertinence is enhanced.

One embodiment of the present disclosure provides a method for calculating polygenic risk scores. The method comprises the steps of: calculating a phenotype estimation value for genome data of an individual by using a pre-set first algorithm; classifying, into a plurality of groups according to a specific criteria, rare genetic variations for each gene included in the genome data, and estimating the size of the effect of the rare genetic variations by using a pre-set second algorithm based on a genotype matrix for each of the plurality of groups and the phenotype estimation value; and calculating a rare genetic variation polygenic risk score by using a pre-set third algorithm based on the genotype matrix for each of the plurality of groups and the size of the effect of the rare genetic variations.

Systems, apparatuses, methods, and computer program products are disclosed for generating an admixed PRS for an admixed subject. An example method includes assigning an ancestry label to one or more phased subject genotype segments and generating one or more ancestry specific sets. For each ancestry specific set, the method further includes applying a polygenic risk model to each phased subject genotype segment of the ancestry specific set to generate one or more ancestry specific raw partial PRSs, applying the polygenic risk model to corresponding unadmixed genotype segments to generate one or more unadmixed ancestry raw partial PRSs, determining a mean PRS and a standard deviation PRS for the unadmixed ancestry cohort, normalizing the one or more ancestry specific raw partial PRSs to generate normalized partial PRSs, and generating the admixed PRS for the admixed subject based on a weighted sum of the normalized partial PRSs for each ancestry specific set.

The application relates to the technical field of medical data processing, and discloses a construction method and system of a model for predicting the risk of metabolic syndrome caused by antipsychotic drugs, which comprises the following steps: acquiring clinical information and metabolic index data of a discovery cohort and a verification cohort respectively; acquiring genome data of the discovery cohort, screening significant SNP sites through whole genome association analysis, and calculating a polygenic risk score; acquiring proteome data of the verification cohort, screening candidate proteins through metabolic outcome association analysis; taking PRS data and / or candidate protein level data as independent variables, and taking the percentage change of metabolic indicators as a risk label to train a prediction model. The application integrates multi-omics information of genomes and proteomes to construct a prediction model capable of quantitatively evaluating the risk of metabolic syndrome caused by antipsychotic drugs for individuals, which can assist doctors in accurately identifying high-risk patients before or in the early stage, and provides an important technical tool for formulating individualized treatment plans and reducing the risk of MetS in clinical practice.

The present invention discloses a genomic biomarker combination for diagnosing keratoconus. Based on the whole-genome sequencing data of keratoconus, the present invention conducts association analysis, screens significant variant sites related to keratoconus, and performs polygenic risk score modeling and susceptibility risk prediction. The results show that the genetic variant risk sites screened by the present invention can effectively distinguish between normal populations and keratoconus populations.

This provides a method to reduce cardiovascular risk in patients with a genetic profile associated with a response to proprotein converter subtilisin / kexin type 9 (PCSK9) inhibitor therapy. [Solution] A method for treating a patient at risk of major cardiovascular adverse events (MACE) includes: determining the patient's polygenetic risk score (CAD-PRS), which includes a weighted sum of multiple gene variants associated with coronary artery disease; identifying the patient as being at high risk of MACE if the patient has a CAD-PRS greater than a threshold CAD-PRS determined from a reference population; and administering a PCSK9 inhibitor to the patient if the patient has been identified as being at high risk of MACE.

The application discloses a dyslipidemia risk prediction device and terminal equipment, method and storage medium, and relates to the technical field of medical detection equipment. The device comprises an acquisition module, a first processing module and a second processing module which are connected with each other. The first processing module is used for inputting SNP genotype data of a to-be-predicted object into a pre-constructed scoring model, obtaining a polygenic risk score, and inputting an actual age and a phenotype age into a pre-constructed age linear regression model after obtaining the phenotype age based on clinical biochemical marker data and the actual age of the to-be-predicted object, so as to obtain a phenotype age acceleration value. The second processing module is used for inputting the polygenic risk score, the phenotype age acceleration value and individual characteristic data into a pre-constructed integrated model, obtaining a prediction result set, and taking a mode of the prediction result set as a prediction result of the to-be-predicted object. The device can be used for more comprehensive and accurate prediction and can improve processing efficiency.

The application provides an atrial fibrillation prediction method based on deep learning and multi-gene scoring, which combines electrocardiogram (ECG) signals, clinical feature data and multi-gene risk score (PRS) based on multi-modal data fusion. By integrating deep learning models, clinical risk scores and multi-gene risk scores, the accuracy and stability of AF prediction are significantly improved. Its technical value lies in: multi-source data fusion: for the first time, ECG signals, clinical data and genetic information are combined in a single system to provide comprehensive individualized risk assessment; real-time analysis capability: based on the real-time data processing capability of ECG, it can be used for dynamic risk assessment; clinical decision support: providing scientific and quantitative AF prediction reports for medical staff to assist in developing intervention strategies.

The present application relates to a kind of coronary risk prediction method and system of coronary heart disease patient, to solve the limitation that traditional static evaluation model is difficult to integrate multi-source dynamic data. The multi-source heterogeneous data of patient is collected from hospital information system, gene database and wearable device;Key features are extracted by special processing module respectively: unstructured text and image plaque features are analyzed using deep learning model, multi-gene risk score is calculated, and time series analysis is used to capture physiological parameter attenuation trend, a three-level cascade integrated model is constructed, the first level filters high contribution degree features, the second level integrates space-time dynamic features through attention mechanism network, the last level integrates static and dynamic factors to generate dynamic risk score, and output visual risk trend chart and high-risk factor heat map, real-time early warning is generated through medical terminal and individualized intervention scheme is generated. The present application realizes the collaborative analysis of multidimensional data and dynamic risk tracking, significantly improves early warning capability and clinical decision efficiency.

The invention discloses an SNP (Single Nucleotide Polymorphism) marker combination related to chronic obstructive pulmonary disease risk assessment, an integrated risk assessment model and application thereof. According to the invention, 22 SNP loci significantly related to chronic obstructive pulmonary disease susceptibility are creatively screened through large-scale Meta analysis, and a high-precision prediction model fusing polygene risk score (PRS), demographic characteristics, smoking behaviors and lung function reference parameters (FVC / FEV1 predicted values) is constructed for the first time. The model overcomes the defects that the prior art depends on a single factor and is insufficient in prediction precision, a personalized risk assessment result can be output by inputting individual multi-modal information, a powerful tool is provided for early warning and targeted prevention of chronic obstructive pulmonary disease, and the model has remarkable clinical application value and public health significance.

The present disclosure relates to a device and method for predicting the risk of disease occurrence by utilizing single nucleotide polymorphisms and the presence or absence of monogenic variants in a subject. According to the device and method according to an aspect, prediction of the risk of disease occurrence is enabled based on a more accurate genetic risk by integrating together monogenic variants, which are based on the subject's genetic information and have a clear causal relationship but appear rarely, and the polygenic risk score, which is based on commonly occurring single nucleotide polymorphisms that, in comparison, do not have high individual association.

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.

Systems, devices, methods, and computer program products are disclosed for generating a mixed lineage PRS for a mixed lineage subject. An example method includes assigning an ancestor tag to one or more phasing subject genotype segments; and generating one or more ancestor specific sets. The method further includes, for each ancestor specific set, applying a multi-gene risk model to each phasing subject genotype segment of the ancestor specific set to generate one or more ancestor specific original portion PRSs; applying the multi-gene risk model to the corresponding non-mixed lineage genotype segment to generate one or more non-mixed lineage ancestor original part PRS; determining a mean value PRS and a standard deviation PRS for the non-mixed bloodline ancestor queue; normalizing the one or more ancestor-specific original partial PRSs to generate a normalized partial PRS; and generating a mixed lineage PRS for the mixed lineage subject based on a weighted sum of the normalized partial PRS for each ancestor specific set.

The present application relates to the field of forensic pathology, forensic genetics and molecular identification technology, in particular to a primer composition for evaluating genetic susceptibility of unexplained sudden death, products and applications thereof. The primer composition provided by the present application comprises nucleotide sequences such as amplification primers shown in SEQ ID NO. 1-512, that is, 256 pairs of primers related to 238 quantitative trait loci. The present application further provides products and methods for evaluating genetic susceptibility of unexplained sudden death of individuals. Experimental results show that the primer composition or kit provided by the present application can detect consistent and highly repeatable genotyping results, and on this basis, the genetic susceptibility of unexplained sudden death can be further evaluated by calculating the polygenic risk score, which has important significance for the genetic background research of unexplained sudden death and the accurate cause of death identification in sudden death related cases in forensic identification work.

The invention provides a multisystem disease analysis method based on gastrin plasma proteomics, which comprises the following steps of: A, analyzing a large number of adult individuals subjected to baseline evaluation by using queue research data of a biological sample library, and measuring a plurality of plasma proteins; b, analyzing a standardized protein expression (NPX) value of gastrin (GAST) by adopting a multivariable Cox proportional risk model, and correcting by using Bonferroni so as to reduce a false positive result; and C, exploring the correlation between the GAST-PRS and the disease phenotype by adopting full phenotype correlation analysis (PheWAS) based on polygene risk score (PRS), and further analyzing the genetic correlation between the GAST and the disease by using linkage imbalance score regression (LDSC). According to the method, the GAST can be redefined as a cross-system risk regulation factor, and the method is of great significance to layering of patients with shared pathway imbalance. The GAST has multi-system correlation with morbidity and mortality in the outcome of 168 diseases, and challenges the classical normal form of the gastrointestinal specific function of the GAST. These findings establish the location of GAST as a multi-system risk indicator.