90 results about "Genetic risk" patented technology

The invention belongs to the technical field of biomedical image processing and analysis, and particularly discloses a chromosome abnormality detection method based on image analysis, which comprises the following steps: obtaining a user chromosome microscopic image set and extracting a multi-modal feature data set for processing to obtain abnormal chromosome feature data of an abnormal tracking area; and performing collection processing to obtain an abnormal risk assessment index, performing correction in combination with abnormal identification coefficients of a plurality of historical similar abnormal samples to obtain an abnormal risk correction index, and performing assessment processing according to an obtained clinical detection data set corresponding to the abnormal tracking area in combination with genetic background feature data to obtain an abnormal tracking area genetic risk judgment index. And comparing the abnormal risk correction index with the abnormal risk correction index to obtain an abnormal evaluation matching degree coefficient, and verifying an abnormal evaluation result through threshold comparison, so that efficient identification and accurate evaluation of various complex chromosome abnormality types are realized, and comprehensive risk evaluation is performed in combination with genetic background information and clinical data to improve the reliability of a detection result.

The invention provides a gene detection and phenotype verification health detection method and system, and the method comprises the following steps: collecting multi-source data of a user, locking genetic variation highly related to disease and individual specificity through gene sequencing, and building an individualized genetic risk scoring system; wherein the multi-source data comprises gene sequencing and proteomics data of a user; performing gene phenotype coupling analysis, integrating multi-dimensional data of genes, proteins or phenotypes of the user, and constructing a health assessment system with high confidence; the intervention decision generation is used for generating a decision to intervene the user; through gene-phenotype coupling analysis, the disease risk prediction specificity of healthy people is improved, compared with a conventional health monitoring technology, risks are found in advance, the health behavior compliance rate of a user is increased, seamless connection with mainstream wearable equipment and an electronic medical record system is supported, and the utilization rate of data is increased.

The invention provides a method for screening disease-related gene targets based on whole genome association analysis, and belongs to the technical field of disease genetic risk assessment, the method comprises the following steps: collecting and recording a sample information data set; performing whole genome sequencing on the samples to obtain SNP data, and combining the SNP data of all the samples to obtain a training data set; performing principal component analysis on the SNP matrix, and screening the top 10-30 SNPs as covariables of the model; and carrying out whole genome association analysis on the training data set and constructing a logistic regression model, and screening out the SNP related to the disease according to the P value. According to the method, confounding factors can be effectively controlled by adopting the logistic regression model and introducing covariables (such as age, gender and the number of weekly exercises), high-dimensional data are processed through a regularization method, and the risk of over-fitting is reduced. The method can also capture interaction between SNPs, and provides more accurate disease-related SNP recognition.

The invention relates to an intelligent inquiry and risk evaluation method for gynecological tumors. The method comprises the following steps: acquiring symptom information of a consultant and tumor medical history information of relatives in a plurality of generations of families of the consultant; constructing a tumor genetic family diagram by using the tumor medical history information, and then evaluating genetic risks of consultants to generate genetic consultation suggestions; constructing a symptom semantic network based on a medical ontology, and converting the symptom information into standard symptom data; calling a corresponding hierarchical inquiry protocol according to the standard symptom data to generate an intelligent diagnosis result; and integrating the genetic risk assessment result and the intelligent diagnosis result and outputting a diagnosis suggestion. Gynecological tumor inquiry consultation and risk evaluation can be automatically completed, and meanwhile a clinical research form is generated and inserted into a hospital electronic medical record system. The whole process does not need manual operation of doctors, the workload of information collection and input of the doctors is greatly reduced, and the doctors can concentrate on the core diagnosis and treatment process.

The invention discloses a schizophrenia risk structure variation identification and function evaluation method based on three-generation sequencing, and relates to the field of molecular biology, whole genome sequencing is performed on peripheral blood DNA of a patient through three-generation sequencing, multi-tool joint detection is adopted, multi-sample results are integrated, and a high-confidence SV data set is generated; through cross-queue comparison, the patient specific SV is screened, and the high-risk potential pathogenic SV is identified in combination with an SV priority ordering tool SVJudge. By combining transcription factor binding analysis, SCZ drug target data and histocyte specific expression data, the influence of SV on gene regulation is evaluated, and the potential action mechanism of SV in SCZ is analyzed. The SCZ risk gene is screened based on SVJudge scoring and patient carrying conditions, the genetic risk and pathogenic mechanism of the SCZ are analyzed and verified through pathway enrichment, a protein interaction network and a functional module, and a new technical means and theoretical basis are provided for genetic research of the SCZ.

The invention provides a teenager disease risk dynamic prediction method and system, and the method comprises the steps: obtaining an electronic health record and subcutaneous metabolite dynamic concentration data of a teenager, and the electronic health record comprises chronic disease related information, a genetic risk value and historical diagnosis data; performing multi-modal fusion on the metabolite concentration data, the genetic risk value and the historical diagnosis data according to a time axis to form a mixed time sequence information set; adaptive optimization is carried out on the mixed time sequence information set, and the collaborative change trend of metabolite fluctuation characteristics and genetic characteristics is identified from the optimized mixed time sequence information set; generating a dynamic graph by adopting a graph neural network based on the collaborative change trend; and tracking an abnormal conduction chain of a metabolic pathway in the dynamic map in real time, and calculating a risk probability matched with the growth stage of the teenagers according to the abnormal conduction chain and chronic disease related information. The dynamic prediction precision and timeliness of the chronic disease risk of teenagers are improved.

Methods, kits, and systems for assessing the risk of a human subject for developing a cancer, including genetic risk assessment, clinical risk assessment, and combinations of both to improve risk analysis. Technologies utilize, among other things, analyzing a sample of DNA obtained or derived from a subject to detect the genotype for a plurality of test genomic loci.

The invention discloses a joint detection kit for genetic risk genes and medication genes of Alzheimer's disease and a multivariable risk assessment model. The kit comprises a detection reagent for detecting 25 mutation sites of 15 genes, wherein the 25 mutation sites of the 15 genes comprise 17 mutation sites of 10 risk genes and 10 mutation sites of 6 medication genes. Amplification primer pairs and single-base extension primers of each site are designed for 25 mutation sites of 15 genes, multiple PCR amplification and single-base extension reactions are carried out, and the genotype of each site of a product is analyzed by using matrix-assisted laser desorption ionization time-of-flight mass spectrometry. A multivariable risk assessment model for the Alzheimer's disease is constructed by taking a genetic risk score GRS, age, gender and plasma p-tau217 concentration of a risk gene mutation site as markers, one-stop detection of'early screening and medication guidance 'can be realized in combination with a medication gene detection result, and the application prospect and demand are broad.

The invention relates to the technical field of biology, and provides a genetic marker for children nephrotic syndrome genetic risk assessment. The invention relates to genetic detection and application of hormone sensitive nephrotic syndrome (pSSNS) of children. Nine risk sites, including new sites of 1q23.1, 1p36.13, 5p13.2, 10q21.3, 10q24.1 and the like, highly related to diseases are found by integrating whole genome association research (GWAS) data, combining Meta analysis and a conjugate false discovery rate (conjFDR) method and taking genetic information of IgA nephropathy as assistance. Research results show that genes near the loci have differential expression in pSSNS and IgAN patients, which prompts that the genes play an important role in the occurrence and development of diseases. The invention provides a molecular detection method based on the risk site, which can be used for risk assessment, auxiliary diagnosis and prognosis prediction of children's nephropathy. Meanwhile, the invention provides potential application values of the loci and related genes thereof in individualized medication and targeted therapy.

The invention discloses a cardiovascular and cerebrovascular disease information management method and system, and relates to the technical field of genomics, and the method comprises the steps: collecting health information and a biological sample of a patient, and generating a health file; performing genomics analysis by using the generated health archive, and calculating a genetic risk score of the patient; according to the genetic risk score of the patient, microvascular lesion detection is carried out on the high-risk patient, microvascular anomaly indexes are quantified, and an individualized health risk score is generated; metabonomics analysis is carried out based on the health risk score, and the metabolic state and metabolic capability of the patient are evaluated; combining the metabolism assessment result, the genetic risk score and the health risk score to formulate an individualized intervention scheme; the problems of data isolation and intervention generalization in the prior art are solved, and powerful technical support is provided for early prevention and accurate management of cardiovascular and cerebrovascular diseases.

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 present invention discloses a method for cancer pre-screening of a subject. Genetic risks associated with genetic characteristics are determined by sequencing and analyzing cell free DNA ("cfDNA") fragments present in a blood sample of the subject. The clinical risk is determined based on factors such as age, gender, and race. In some cases, clinical factors specific to certain cancers, such as smoking conditions, are combined. By incorporating clinical risks into genomic risk analysis, improved lung cancer pre-screening results are provided, enabling the same number of positive cancer detections to be achieved using a small number of LDCT lung cancer screening times.

The application discloses a model construction method and equipment for evaluating the risk of occult HBV infection of a subject and application. According to an embodiment of the application, 24 SNP sites in HLA gene regions are obtained by a specific screening method, and the 24 SNP sites are used as molecular markers of occult HBV infection (OBI) and are used for model construction, so that genetic risk evaluation of OBI is realized. Experimental results show that the evaluation index AUC of the application is 0.86, and the application has the characteristics of high specificity, high sensitivity and high accuracy, and can provide more comprehensive, accurate and individualized evaluation for the risk of OBI.

The present invention belongs to the field of genetic risk prediction technology and discloses a method for constructing a genetic risk prediction model integrating functional annotation information and its application, comprising: calculating the heritability of the specific annotations of each cell type corresponding to each tissue of the sample to be tested based on the marginal chi-square statistic of each SNP, and then obtaining the heritability of the j-th SNP in the current tissue f and estimating the joint effect size b of M SNPs in tissue f. f , and based on b f The covariance between the phenotype vector y of the n samples to be tested is b f The optimal linear unbiased estimate of the genetic risk prediction model is then obtained. Furthermore, the genetic risk scores corresponding to each tissue are integrated, and the functional annotation information of each tissue is incorporated into the genetic risk prediction. A genetic risk prediction method integrating functional annotation information is also provided. The present invention can improve the accuracy of genetic risk prediction.

The specification provides a genetic risk feature screening method, a prediction method and a system, and relates to the technical field of medical diagnosis. The method comprises determining a plurality of genetic risk features related to a target variable of a sample and an NT value of the sample; grouping the features by a machine learning model according to the characteristics of the genetic risk features related to the target variable; determining an NT value interval; converting the NT value of the sample into an NT feature value of the sample according to the NT value interval; and screening key features for genetic risk determination by the machine learning model with the NT feature value of the sample and each feature group as input and with the NT feature value of the sample and the plurality of feature groups as input. The combination of the characteristics of the genetic risk features and the feature grouping by the machine learning model can effectively reflect the relationship between the features, effectively screen out key features for accurately predicting genetic risk, assist medical diagnosis, and greatly reduce the cost of diagnosis time.

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.

This application proposes a probe assembly, reagent kit, and their applications for detecting knee osteoarthritis, belonging to the field of biomedical detection technology. It addresses the technical problems of existing methods for detecting the risk or pathogenesis factors of knee osteoarthritis, such as slow speed, low throughput, and high cost. The probe assembly includes 112 probe sequences for detecting 56 target genes. The nucleotide sequences of these 112 probe sequences are shown in SEQ ID NO.1~SEQ ID NO.112, with at least two probe sequences corresponding to each target gene. The 56 target genes include 20 genes related to bone metabolism and cartilage formation, 19 genes related to inflammation, 8 genes related to extracellular matrix, and 9 genes related to drug metabolism and efficacy. This application features high throughput, speed, and accuracy. It can be applied to genetic risk assessment of knee osteoarthritis.

The application provides a probe composition for detecting genetic cardiovascular diseases and application thereof. The probe composition for detecting genetic cardiovascular diseases comprises 21201 probes which are identical or complementary to the sequences between corresponding sites on chromosomes of a human reference genome hg19. The probe composition for detecting genetic cardiovascular diseases can detect coding regions of 122 genes related to genetic cardiovascular diseases and non-coding regions of 29 genes at one time, realize detection of various mutation types of five types of genetic cardiovascular diseases, i.e. cardiac ion channel diseases, genetic cardiomyopathy, genetic aortic diseases, pulmonary arterial hypertension and familial hypercholesterolemia, the detection content has wide coverage, the detection efficiency is high, and therefore the occurrence and genetic risk of genetic cardiovascular diseases can be comprehensively evaluated, which has important significance for early prevention, early intervention and auxiliary diagnosis.

This invention provides a localized differential privacy protection method for medical data. It employs a perturbation algorithm to perturb the data, with the privacy budget set as follows: sensitivity levels are applied to the relevant disease types; based on the sensitivity level, a corresponding privacy budget is set for each disease, where higher sensitivity levels correspond to smaller privacy budget values. This invention assesses disease sensitivity from multiple dimensions, including genetic risk, social discrimination, and treatment costs, achieving differentiated dynamic privacy budget allocation. Smaller values ​​are allocated to highly sensitive diseases to enhance privacy protection, while larger privacy budget values ​​are allocated to less sensitive diseases to reduce data distortion. This solves the problem of insufficient targeted protection caused by fixed privacy budgets in existing methods. Furthermore, the accuracy against background knowledge attacks is significantly lower than 1.2 times that of random guessing, and the privacy protection effect meets the stringent requirements of the formal definition of localized differential privacy.

This invention belongs to the field of myopia prevention and control technology, and relates to a method, device, and medium for myopia prevention and control based on genetic and environmental factors. Based on myopia data of each blood relative of the user to be intervened, the variant sites in the myopia-related single nucleotide polymorphism genotype, and the number of risk alleles, a myopia genetic risk index is calculated. Based on the user's daily outdoor activity time, total daily eye use time, daily near-vision time, and daily low-light eye use time over several consecutive days, an outdoor activity stability index, a near-vision load index, and an eye use light stability index are calculated, thus obtaining an environmental myopia risk index. The axial length, refractive error, myopia genetic risk index, and environmental myopia risk index of the user to be intervened are input into an adaptive Bayesian myopia risk prediction model, which outputs the myopia risk change trajectory under different myopia intervention intensities, including the predicted mean and uncertainty interval, thereby obtaining a myopia intervention strategy.

This invention belongs to the field of SNP site detection technology for guiding the use of second-generation antipsychotic drugs. Specifically, it relates to primer and probe combinations and applications for SNP site detection to guide personalized medication of second-generation antipsychotic drugs. The SNP site is rs17782313. The primer and probe combination includes an upstream primer SEQ ID NO:1 that specifically amplifies the wild-type T allele, an upstream primer SEQ ID NO:2 that specifically amplifies the mutant C allele, a universal downstream primer SEQ ID NO:3, a wild-type probe SEQ ID NO:4, and a mutant probe SEQ ID NO:5. This invention assesses the genetic risk of weight gain and metabolic disorders in patients with mental illness after taking second-generation antipsychotic drugs such as risperidone, quetiapine, amisulpride, and paliperidone through rapid and accurate genotyping. It features high specificity, high accuracy, speed, simplicity, and controllable cost, and can achieve rapid single-tube genotyping, providing key genetic evidence for the clinical development of personalized medication regimens.

The present invention relates to the field of biomedicine, and particularly to the application of protein markers in the preparation of products for predicting the future incidence risk of coronary heart disease in subjects. The present invention provides an application of using 11 proteins as markers in the preparation of products for effectively distinguishing and predicting the future incidence risk of coronary heart disease in people without traditional risk factors. After this model is extended to the entire population, it can further improve the predictive ability of coronary heart disease on the basis of traditional risk factors or genetic risks, effectively identify and stratify the incidence risk of coronary heart disease, thereby improving primary prevention. The present invention can truly achieve individual coronary heart disease risk assessment, and thus can achieve better risk prediction or assessment in both the entire population and those who have been misjudged as healthy or at low risk in the past.

The present invention relates to a method of determining whether a subject is at risk of developing type 1 diabetes by determining the genetic risk score (GRS) of a subject. The present invention also comprises a pharmaceutical composition comprising insulin and a pharmaceutical acceptable carrier for use in a method for preventing type 1 diabetes in a subject having a genetic risk score as determined by the method mentioned above. Further, it encompasses a kit for use in a method of determining whether a subject is at risk of developing type 1 diabetes by determining the genetic risk score of a subject and a type 1 diabetes antigen for use in a method of immunizing a subject against type 1 diabetes having a genetic risk score as determined by the method mentioned above.

A computerized system and method are provided for assessing a number of gene-gene interactions between the HLA and IRF5 gene regions. At least one computing device enrolls subjects in a registry, including SLE patients having met classification criteria for SEE and Sjogren's patients having met AECG criteria. Moreover, at least one computing device can perform genotyping for the subjects and healthy control subjects, for submission to a genotyping platform. Further, at least one computing device can develop HLA risk factor models for each of a plurality of stages, and perform statistical analysis for each of the plurality of stages.

The present application provides a method, electronic device, and storage medium for constructing an obesity genetic risk model based on a stacking model. The method comprises: obtaining a data set, wherein the data set comprises genetic feature data, wherein the genetic feature data comprises single nucleotide polymorphisms; applying a random forest to screen the single nucleotide polymorphisms to obtain a first genetic feature set related to the genetic risk of obesity; applying a distributed gradient boosting library model to screen the single nucleotide polymorphisms to obtain a second genetic feature set related to the genetic risk of obesity; obtaining a significant genetic feature set based on the first genetic feature set and the second genetic feature set; constructing an obesity genetic risk model; obtaining a model evaluation result of the obesity genetic risk model, and optimizing the parameters of the obesity genetic risk model based on the model evaluation result. By integrating multiple basic learners and combining meta-classifier logistic regression for stacking training, the advantages of different algorithms are effectively integrated, thereby improving the accuracy and stability of obesity genetic risk prediction.

A personalized multi-factor genetic risk system for disease prediction and method of use is described herein. The personalized multi-factor genetic risk system is generated by generating a disease specific polygenic score model. Responsive to receiving one or more inputs, wherein the one or more inputs include genomes or partial genomes of an individual, the disease specific polygenic score model generates a lifetime disease likelihood for the individual.

The invention provides an SNP (Single Nucleotide Polymorphism) genetic marker combination for predicting the risk of motion sickness, a kit and application of the SNP genetic marker combination in a calculation model and a computer system. The combination comprises at least one marker which is selected from the following core SNP (Single Nucleotide Polymorphism) sites: chr1. 62113791A, chr2. 331627594T, chr3. 125291088T and chr2. 3093462T, and is characterized in that the core SNP sites of the chr1. 62113791A, the chr2. 331627594T, the The kit is used for carrying out genotype determination based on a Real-t time PCR technology, and is combined with a weighted genetic risk scoring (OR-GRS) model to realize grading evaluation on the sensitivity of the motion sickness of an individual. It is verified and displayed in an independent crowd queue that the prediction accuracy of a high-risk group is high, the prediction accuracy of a low-risk group is also high, and good sensitivity and specificity are achieved. The system can be widely applied to the fields of navigation, aviation, traffic medicine and individualized early warning management.

The invention relates to the technical field of treatment of neurodegenerative diseases, in particular to application of USP6NL in regulation of TGM7 protein. The invention relates to an application of USP6NL protein or a coding gene thereof in preparation of medicines for treating AD (Alzheimer's disease). The amino acid sequence of USP6NL is at least one of SEQ ID NO.1-SEQ ID NO.3; the invention relates to an application of a TGM7 protein in preparation of a medicine for treating AD (Alzheimer's disease), an amino acid sequence of the TGM7 is at least one of SEQ ID NO.4-SEQ ID NO.5, an application of a USP6NL protein in promotion of TGM7 secretion and an application of a USP6NL protein in promotion of A beta oligomerization. According to the application of the USP6NL in regulating the TGM7 protein provided by the invention, the previously unknown relation between the USP6NL-TGM7 axis as intracellular transport and extracellular A beta aggregation is disclosed, the USP6NL as a functional genetic risk factor of AD is determined, and the pathway is named as a treatment target which has a prospect and can be used for drug treatment.

The invention relates to the technical field of genetics, in particular to a genetic risk assessment system which comprises a control terminal which is a main control terminal of the system and is used for sending out an execution command; the collection module is used for collecting genetic risk related parameters of the user; the analysis module is used for traversing the user genetic risk related parameters collected in the collection module and analyzing the similarity of the user genetic risk related parameters; the identification module is used for acquiring an analysis result of the similarity of the user genetic risk related parameters in the analysis module, and identifying the user genetic risk related parameters with the best user matching degree based on the analysis result of the similarity of the user genetic risk related parameters; according to the invention, by uploading, comparing and analyzing the relevant parameters of the genetic risk of the user, the early prevention condition of the genetic disease is effectively provided for the user, and the health management of the family of the user is facilitated, so that a better genetic disease prevention effect is brought to the next generation of user inoculation.

The invention relates to a primer group, a kit and a method for detecting a Fabry cardiomyopathy genetic susceptibility gene, and belongs to the technical field of gene detection. The genetic susceptibility gene of the Brie cardiomyopathy is a GLA gene, and the mutation site of the genetic susceptibility gene is c.640-801Ggt; a; the primer group comprises a PCR (Polymerase Chain Reaction) amplification primer group, and the amplification primer group comprises a forward PCR amplification primer and a reverse PCR amplification primer; the primer group further comprises a Sanger sequencing primer group, and the sequencing primer group comprises a forward sequencing primer and a reverse sequencing primer. The invention also provides a kit and a detection method based on the primer group. According to the primer group and the kit containing the primer group, the c.640-801Ggt of the Fabry cardiomyopathy GLA gene can be detected on the basis of a first-generation sequencing technology; a site mutation has important value for early screening detection and genetic risk assessment of the disease gene.