22 results about "Geo database" patented technology

The invention discloses an experimental method for relation between cytokine level and tumor immune microenvironment in circulation, which comprises the following steps of: S1, collecting colorectal cancer patient data and normal data for contrast, and preferably screening a gene expression data set from a Gene Expression Omnibus (GEO) database; according to the method, a plurality of gene expression data sets from the GEO database are collected and analyzed, so that differential expression genes between colorectal cancer and normal tissues can be systematically identified, and a solid foundation is provided for subsequent research. The process not only covers batch correction, normalization and batch effect elimination of data, but also visually presents data distribution changes through principal component analysis, ensures the accuracy and reliability of analysis results, confirms that IL6 and IFN gamma are one of the most critical factors affecting the colorectal cancer immune microenvironment, and participate in formation of immune infiltration.

The invention discloses a method for establishing a channel element spatial database, which comprises the following steps of: determining a data object of channel element data acquisition so as to extract feature information to establish a channel element data model; based on the channel element data model, determining spatial geometric information of the data object, and establishing a channel element spatial data model; analyzing the logic relation between the data objects on a model level to establish a channel element spatial data logic model; and on the basis of a Geodatabase spatial database, carrying out physical conversion on the channel element spatial data logic model, and establishing a channel element spatial database so as to realize the same standard for channel element data and facilitate maintenance of related data. The invention also discloses a navigation channel element data acquisition system, which is based on the navigation channel element spatial database, develops the navigation channel element data acquisition system integrating data acquisition, data quality inspection and result display by adopting a B / S architecture, and realizes standardization, digitization and spatialization of data acquisition.

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 provides a method for predicting multiple degenerative diseases and screening markers based on DNA methylation, and belongs to the technical field of biological information. The method comprises the following steps: firstly, acquiring DNA methylation data from a GEO database, and screening out CpG loci with non-zero feature importance through a random forest algorithm; performing multi-scale feature extraction on the screened CpG sites by using a multi-scale one-dimensional convolutional neural network, and capturing local and global features; then, 110 key CpG loci related to various degenerative diseases are screened out through interaction network analysis of GO, KEGG and protein; and finally, constructing a multi-classification disease prediction model based on the residual network, and performing classification prediction on neurodegenerative diseases, bone degenerative diseases, breast cancers and health states. According to the invention, the screening accuracy of degenerative diseases is improved, a scientific biomarker screening basis is provided, and support of precise medical treatment and early intervention is facilitated.

The invention provides an AI-assisted network pharmacology exploration method for treating liver cancer by Qi-tonifying and Zhongzhong decoction, which is used for revealing a molecular mechanism of the Qi-tonifying and Zhongzhong decoction for treating liver cancer. The research belongs to the field of computer-aided drug design, and aims to systematically promote key links of drug development through a mode of combining multi-database resource integration with an AI technology. Firstly, a plurality of authoritative databases are comprehensively utilized to screen potential active components and target genes thereof, gene expression data in a GEO database are combined, a disease-related gene network is constructed through an AI algorithm, and core target genes for liver cancer treatment are accurately recognized. Secondly, on the basis of a ChEMBL database and an AI auxiliary screening method, candidate compounds highly related to core target genes are mined; afterwards, through a full-automatic molecular docking technology and molecular dynamics simulation, the binding performance and stability of the candidate compound and the core target spot on the molecular level are deeply evaluated, and therefore the stable compound with the excellent binding characteristic is screened out. Finally, the efficacy and safety of the Qi-tonifying and middle-jiao-strengthening decoction are verified through in-vitro experiments, and a theoretical basis and experimental support are further provided for a molecular mechanism of the Qi-tonifying and middle-jiao-strengthening decoction for treating liver cancer. According to the research, on the basis of multi-database resource integration, artificial intelligence and an advanced calculation simulation technology are combined, a systematic process from drug discovery to verification is established, and an innovative method and a practical path are provided for modernization and precise tumor treatment of traditional Chinese medicine.

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 method for researching the influence of exercise training on myelin sheath repair, and relates to the technical field of biology. Comprising the following steps: S1, data collection and analysis: acquiring a spinal cord injury related data set from a GEO database through a bioinformatics method, and determining differential gene expression related to spinal cord injury; carrying out GO enrichment and KEGG function enrichment analysis on the differential genes, taking intersections of the differential genes and cholesterol-related genes, and screening; s2, establishment of an animal model: selecting mice, and randomly dividing the mice into a false operation group and a spinal cord injury group; and constructing a half-side spinal cord injury model for the mice in the spinal cord injury group. The invention provides a new scientific basis and strategy for treatment of spinal cord injury, and particularly emphasizes the importance of exercise training in combination with cholesterol metabolism regulation in promotion of myelin sheath repair; the mechanism of the PGC1 alpha for regulating and controlling the cholesterol metabolic pathway is discussed, and a theoretical basis is provided for exploration in combination with clinical application.

The present invention discloses a method for evaluating storage utilization of a digital twin model, comprising the following steps: S1, collecting and modeling vertical warehouse space data; S2, constructing a digital twin vertical warehouse model based on the spatial data model; S3, evaluating and analyzing storage utilization based on the spatial data model, which uses spatial division and measurement methods to evaluate the utilization of the vertical warehouse space, and compares and analyzes the storage utilization rates of different areas to find out areas with insufficient or wasted space. The present invention uses spatial division and measurement methods to evaluate the utilization of the vertical warehouse space, which not only realizes the visual expression of space utilization, but also can calculate the storage utilization rate of the vertical warehouse from different dimensions such as shelf use, channel utilization, and overall space, so it can greatly improve the multi-dimensionality and precision of space utilization.

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.

The present invention discloses a biomarker for patients with peritoneal metastasis of gastric cancer and its application, and relates to the field of biomedicine technology. The present invention provides a biomarker SMOC2 for patients with peritoneal metastasis of gastric cancer. By mining the public data set GSE62254 for gastric cancer, it is found that the expression of SMOC2 in the tumor tissue of patients with peritoneal metastasis is significantly increased, and patients with peritoneal metastasis with high expression of SMOC2 are closely associated with a worse survival prognosis. The present invention provides the application of the biomarker SMOC2 in tumor angiogenesis. Through the analysis of the GEO database and the TCGA database, it is found that the expression of SMOC2 is related to the expression of CD31 and CD34. In gastric cancer tissues with high expression of SMOC2, the number of microvessels is relatively large, which is a potential anti-angiogenesis target.

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 invention discloses a clinical decision support method for identifying smoke disease based on biomarkers in blood. The method comprises the following steps: firstly, determining differential genes between a smoke disease patient and a healthy control group by analyzing a data set in a GEO database; biological pathways and disease types enriched by the differential genes are further analyzed; then identifying a biomarker related to the smoke disease through LASSO and SVM-RFE machine learning algorithms, and evaluating the predictive ability of the biomarker through an ROC curve; finally, screening to obtain CFD and DKFZp434L192 genes, and constructing a nomogram based on the expression of the two genes in the data set so as to predict smoke disease high-risk population in general population. Clinical samples are further collected, expression of CFD and DKFZp434L192 genes in whole blood of smoke disease patients is verified through RT-qPCR, and the technical problem that at present, no efficient recognizable cause of smoke disease exists is solved. By analyzing the expression of the characteristic gene in the blood of the patient, the high-risk group of the smoke disease is quickly predicted, and decision support is provided 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 present invention discloses an integrated line intelligent monitoring system and method, which belongs to the field of smart grid technology, and specifically includes: by collecting current, temperature waveforms and multi-dimensional environmental parameters at the connection points of the transmission line, after preprocessing, using feature extraction to build a dynamic fault waveform feature library; using an AI enhanced algorithm to automatically determine the fault type, and building a temperature prediction model based on time series analysis and machine learning to achieve early warning of temperature anomalies; after the warning is triggered, using Beidou satellite navigation to accurately locate the fault point, and through telemetry short messages combined with adaptive retransmission, efficiently returning fault data to the control center; and continuously combining Beidou positioning and map services to build a monitoring line spatial data model, realize visual display of monitoring data, and improve the intelligence and efficiency of transmission line operation and maintenance.

The invention belongs to the technical field of medical biomolecules, and particularly discloses a column diagram construction method for osteoporosis accompanied with atherosclerosis, which specifically comprises the following steps: acquiring a GSE1428 data set, a GSE230665 data set and a GSE1000927 data set from a GEO database; screening out differential expression genes between the osteoporosis and normal tissues and between the atherosclerosis and healthy tissues by using a Limma bag; then performing WGCNA analysis on DEGs, and finally screening differential expression genes by using an LASSO regression algorithm, a random forest and an SVM-RFE algorithm to obtain a potential key common biomarker; based on the key common biomarkers, a column diagram is constructed, an ROC curve is drawn, and the diagnostic ability of candidate common biomarker genes is evaluated to reach 0.99. Based on the column diagram, a prediction system is provided, and a visual basis is provided for clinical diagnosis of osteoporosis with atherosclerosis.

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.