12 results about "Geo database" patented technology

The invention discloses a biomarker combination for predicting lung metastasis of breast cancer and application of the biomarker combination. The biomarker combination for predicting lung metastasis of breast cancer comprises MUC16, MAP3K13, NR4A3, FAM111A, ZNF562 and NRP1. According to the invention, a GEO database and a TCGA database are combined, a biomarker combination for predicting lung metastasis of breast cancer is obtained through analysis and screening, and potential gene characteristics closely related to development of lung metastasis of breast cancer patients are further determined by analyzing expression levels of six biomarker genes and interaction with a tumor environment. And a more effective and personalized tool is provided for prediction and treatment of breast cancer.

The invention discloses a lung adenocarcinoma prognosis and immune infiltration related prediction model. By deeply mining data in TCGA and GEO databases, a prognosis model based on eight pyroptosis related genes is constructed, and compared with a traditional method, the five-year survival prognosis of a lung adenocarcinoma patient can be more accurately predicted. A verification result shows that the model has relatively high accuracy (AUC = 0.687) in prediction of the total survival of one year, and through Kaplan-Meier analysis and Log-Rank inspection, survival differences of patients in high and low risk groups in a training set and a verification set are remarkably distinguished. The research method and the model have strong universality, provide important reference for research and application of a pyroptosis related mechanism in other cancer types, promote the progress of the oncology field in the research related to the pyroptosis mechanism, and have wide clinical application value.

The present invention discloses a miRNA marker combination, a kit and an application for distinguishing central nervous system lymphoma and glioblastoma in the fields of biomedicine and molecular biology. The miRNA marker combination includes one or more combinations of the following miRNAs detectable in human serum: miR-371a-3p, miR-3202, miR-6757-3p, miR-4763-3p, miR-1915-3p, and miR-3679-5. The present invention uses bioinformatics and molecular biology techniques to perform differential analysis based on blood miRNA chip detection data of PCNSL and GBM in the NCBI-GEO database, establishes candidate serum miRNA markers for PCNSL and GBM, and constructs a risk assessment model, thereby improving detection efficiency and accuracy, reducing the probability of misdiagnosis, and improving patient prognosis.

The invention discloses a system for analyzing a relationship between trialkanoic acid and cognition by using GEO and NHANES databases. The system comprises a GEO database, an NHANES database, a data acquisition module, a data processing module, a GEO data analysis module, an NHANES data analysis module, a data integration module and a data verification module. The signal end of the GEO database and the signal end of the NHANES database are connected with the signal end of the data acquisition module. An analysis result of GEO data and an analysis result of NHANES data are integrated through the data integration module, comprehensive understanding of the trialkanoic acid and cognitive relationship is formed, in the integration process, the module adopts an integration algorithm to ensure the accuracy and consistency of the data, and meanwhile, the data integration module has the advantages of being simple in structure and convenient to use. And the integrated data is compared with a known standard data set or a reliable data source through the data verification module, so that the reliability of the integrated data is further verified, and the problems of data quality reduction and information loss cannot occur after the data is integrated.

The present application relates to a kind of methods based on transcriptome sequencing, machine learning and molecular docking analysis of evodiamine treatment vestibular schwannoma (VS), the present application is analyzed by integrating transcriptome sequencing technology and GEO database, combined with machine learning algorithm, molecular docking and molecular dynamics simulation method, the potential key target and signal pathway of evodiamine treatment VS are systematically analyzed, provide technical support for the mechanism research of evodiamine, and provide reference basis for future individualized treatment strategy of VS.

The invention discloses a sepsis risk prediction method based on intestinal flora exposure factors and a multi-machine learning algorithm, and belongs to the field of sepsis risk prediction.The sepsis risk prediction method comprises the steps that 473 intestinal flora SNP data sets and sepsis data sets are obtained from a GWAS database, a sepsis patient expression microarray data set is downloaded from a GEO database, and a sepsis risk prediction result is obtained; the method comprises the following steps: acquiring positioning information of a gene on a chromosome from a GeneCards database, and acquiring immune infiltration abundance data from CIBERSORT; according to the method, a Mendel randomization method is combined with multiple machine learning algorithms, a sepsis risk prediction model is constructed based on intestinal flora exposure factors, and the intestinal flora exposure factors and target genes closely related to sepsis are screened out through Mendel randomization analysis. A plurality of machine learning algorithms are utilized to construct a model and screen out an optimal model, the accuracy and sensitivity of sepsis early diagnosis are improved, characteristic genes are analyzed, targeted drugs are predicted, and a theoretical basis is provided for precise treatment of sepsis.

This invention discloses a clinical decision support method for identifying Moyamoya disease based on blood biomarkers. First, differentially expressed genes between Moyamoya disease patients and healthy controls were identified by analyzing datasets from the GEO database. Then, the biological pathways and disease types enriched by these differentially expressed genes were further analyzed. Next, Moyamoya disease-related biomarkers were identified using LASSO and SVM-RFE machine learning algorithms, and the predictive ability of these biomarkers was evaluated using ROC curves. Finally, the CFD and DKFZp434L192 genes were selected, and a nomogram was constructed based on the expression of these two genes in the dataset to predict high-risk individuals for Moyamoya disease in the general population. Further, clinical samples were collected, and the expression of CFD and DKFZp434L192 genes in the whole blood of Moyamoya disease patients was verified by RT-qPCR, solving the current technical problem of lacking an efficient method for identifying the etiology of Moyamoya disease. This invention rapidly predicts high-risk individuals for Moyamoya disease by analyzing the expression of characteristic genes in the blood of patients, providing decision support for timely intervention and treatment.

The application belongs to the technical field of biological medicine, and discloses a kind of colorectal cancer metastasis biomarker DPM1 and its application.DPM1 low expression is significantly related to high metastasis risk of colorectal cancer for the first time by the application.Through GEO database analysis and survival curve verification, DPM1 low expression sample has strong metastasis ability and short survival period without recurrence;Transwell experiment proves that overexpression of DPM1 inhibits colorectal cancer cell migration, and knockdown promotes migration;Proteomics and immunohistochemistry of nude mouse model further reveal that it regulates tumor migration phenotype.The application discloses DPM1 as a biomarker for evaluating the risk of colorectal cancer metastasis for the first time, and as a target for developing anti-colorectal cancer metastasis drugs.

The invention provides a rural planning method and system of a multi-source data fusion model, and relates to the technical field of rural planning, and the method comprises the steps: constructing a fusion model of a GIS spatial data model, a BIM model and an inclined three-dimensional model, and presenting a to-be-planned rural three-dimensional scene according to the fusion model and a three-dimensional scene graph data structure; planning index measurement, sunlight analysis, height control analysis and underground space analysis are carried out on the to-be-planned rural three-dimensional scene, a plurality of planning schemes are generated, and the planning schemes are visually presented on the to-be-planned rural three-dimensional scene; and feasibility simulation analysis is carried out on the visually presented planning scheme, fine adjustment is carried out on the planning scheme according to the simulation analysis result, and the intuitiveness and interactivity of the planning result are enhanced through three-dimensional scene display.

The invention provides an Alzheimer's disease biomarker and a screening method thereof. The Alzheimer's disease biomarker is a CD44 gene or a protein coded by the CD44 gene. The screening method of the Alzheimer's disease biomarker comprises the following steps: screening differential expression genes of AD patients through a GEO database, obtaining differentially expressed ferroptosis related genes, carrying out protein interaction network analysis on the differential expression genes of the AD patients related to ferroptosis, carrying out Mendel randomization through machine learning, and screening out the differential expression genes of the AD patients related to ferroptosis; and carrying out human plasma RT-qPCR verification on the randomization result, and selecting a gene which is different from the control group in expression level in the RT-qPCR verification as the Alzheimer's disease biomarker. The CD44 is used as the Alzheimer's disease biomarker, the prediction effect is excellent, a new clue is provided for diagnosis and treatment of AD, and a foundation is laid for revealing complex pathogenesis and developing more effective intervention measures.

The invention discloses a method for developing and evaluating lung cancer detection and diagnosis by using serum proteome data, and relates to the field of computational models.The method for developing and evaluating the lung cancer detection and diagnosis by using the serum proteome data comprises the following steps that S1, a proteomics sample of a GEO database and a lung cancer tissue sample of a TCGA database are integrated, a data set is constructed, and the data set is stored in a database database; wherein a proteomics sample of the GEO database is used as a healthy control group, and a lung cancer tissue sample of the TCGA database is used as a cancer group; compared with the prior art, the method has the beneficial effects that differential proteins in health data and lung cancer data are identified through models constructed by different algorithms, the differential proteins are ranked, and cross-model consistent common proteins are screened based on final Top5 high-importance differential proteins of different models and are used as lung cancer diagnosis core biomarkers; through comprehensive judgment of multiple models, the accuracy of the lung cancer diagnosis core biomarker is ensured.

The invention provides a specific M2-TAMs immunodiagnosis marker related to lung adenocarcinoma and application thereof. The technical key point is that SPAG4 is applied to preparation of a tumor diagnosis product as a diagnosis marker. In the early stage, a WGCNA algorithm is adopted, transcriptome data of lung adenocarcinoma patients in TCGA and GEO databases and M2 type macrophage distribution proportion data analyzed by CIBERSORT are integrated, and an M2 type macrophage polarization correlation module is successfully constructed. Through differential gene screening, Cox regression analysis and Kaplan-Meier survival analysis, the SPAG4 is finally determined as the core regulatory gene. Experimental results show that M2 type macrophage polarization and angiogenesis can be remarkably reduced by inhibiting expression of SPAG4, so that tumor growth is inhibited. On the basis, the advantages of an unmarked metabolism living body imaging technology, a living body blood vessel imaging technology, a living body immunostaining technology and molecular epidemiological research are integrated, the regulation and control mechanism of the SPAG4 gene in the lung adenocarcinoma progress and the influence of the SPAG4 gene on macrophage polarization and blood vessel generation are focused, and a novel tumor diagnosis technology and a kit are developed; the accuracy of early screening of the lung cancer is improved, and a new target spot is provided for precise treatment.