6 results about "Biostatistics" patented technology

The invention relates to a network prediction model for identifying a new cancer gene, a model construction method and application, and belongs to the field of biostatistics. According to the method, genomics, transcriptomics and proteomics data are integrated, a dynamic tensor twinning graph neural network is constructed, a hybrid multilayer random block model is utilized to perform tensor decomposition to extract global and local community features, causal and non-causal information is separated, causal feature mutual information is maximized, and non-causal interference is inhibited, so that the dynamic tensor twinning graph neural network is obtained. The model interpretation force is enhanced; through a dynamic community perception algorithm and a twin graph neural network, inter-layer graph dissimilarity is learned by using a graph similarity function, accurate detection of network structure mutation nodes in a high-frequency dynamic scene is realized, and an independent state transition and co-evolution mode is synchronously positioned. And the biological functions of the candidate genes are verified by combining KEGG pathway enrichment analysis and an independent database. Compared with an existing optimal algorithm, the mutation detection accuracy and the calculation efficiency are remarkably improved, and the AUROC and the AUPRC are both higher than those of an existing advanced recognition method.

The invention relates to the technical field of biostatistics, and discloses a noise language frequency hearing impairment prediction system based on a GEE model and genetic characteristics, and the system comprises a data collection module which collects follow-up visit data of a worker; the data processing module is used for generating a standardized modeling data set; the feature screening module is used for screening features through a statistical learning method to obtain a key feature subset; the time-varying interaction module is used for constructing a generalized estimation equation model and outputting regression coefficient estimation; the risk prediction module outputs the risk probability and the risk layering result of the individual; the decision support module is used for generating hearing protection suggestions of the individuals; according to the method, the generalized estimation equation model is constructed to process follow-up data of occupational noise exposure workers, so that the accuracy and the stability of language frequency hearing loss risk prediction are improved, a basis is provided for formulating a personalized hearing protection scheme and recommending a proper hearing protection device, and the method is suitable for popularization and application. And the transformation of occupational hearing loss from passive treatment to active prevention is facilitated.

The invention provides a recurrent event clinical test sample size estimation method and device, equipment and a medium, and relates to the technical field of clinical test design and biostatistics. According to the method, test basic information is acquired, a statistical model integrating a subject falling mechanism is established based on the test basic information, then key bridge parameters and average exposure time are calculated, and a double-path closed-loop decision including a design stage and an execution stage is executed; calculating a target distribution probability based on the key bridge parameters and the average exposure time so as to calculate a total sample size; and dynamically updating parameters based on the current real-time data in the execution stage, and carrying out self-adaptive random allocation on new subjects. According to the method, the influence of falling of the subject on the exposure time can be explicitly modeled, parameter unified driving sample size estimation and dynamic randomization distribution are introduced, the benefit of the subject is optimized while the inspection effect is ensured, and integrated decision support from design to execution is provided.

The application discloses a tobacco protein disulfide isomerase NbPDI and application thereof in regulation of plant immunity. The application obtains an anti-disease related gene NbPDI01 and a susceptible gene NbPDI23 from tobacco. NbPDI01 and NbPDI23 are overexpressed in tobacco through a transient expression technology, and pepper Phytophthora is inoculated after 2 days. The results show that, compared with a negative control, the disease spot area of the tobacco leaf expressing NbPDI01 is significantly reduced, while the disease spot area of the tobacco leaf expressing NbPDI23 is significantly increased, and biological statistics analysis shows that the difference between the two is significant. It is shown that NbPDI01 can specifically improve the disease resistance of tobacco, and therefore, the NbPDI01 gene provided by the application can be applied to the fields of plant disease resistance breeding, disease resistance gene synthesis and genetic engineering improvement, and provides a new gene resource for improving the disease resistance of crops, and has important theoretical significance and application prospect.

The invention discloses a quasi-real-time discovery method for unknown signals of untoward effects of vaccines based on sequential design and a computer readable storage medium, and relates to the technical field of epidemiology and biostatistics, and the method comprises the following steps: building an untoward event tree, and summarizing event counts per time period according to monitoring time intervals; calculating a leaf-by-leaf expectation according to monitoring data or an external baseline; for each time point and each leaf node, calculating a Poisson generalized log-likelihood ratio and obtaining a p value by using a Monte Carlo method; according to a preset alpha consumption function, giving a corresponding nominal inspection level at the monitoring time; and stopping until a positive signal is found or an upper limit is reached and no positive signal exists. According to the method, timeliness and accuracy can be considered on the premise of ensuring that the overall first type of errors are controlled, conditional / unconditional expectation and leaf-by-leaf output are supported, and the method has the advantages of being easy and convenient to implement, capable of protecting privacy, good in expandability, friendly in engineering and the like.

The present application relates to the technical field of biostatistics, and discloses a noise language frequency hearing loss prediction system based on a GEE model and genetic characteristics, comprising: a data acquisition module that acquires follow-up data of workers; a data processing module that generates standardized modeling data sets; a feature screening module that screens features through statistical learning methods to obtain a key feature subset; a time-varying interaction module that constructs a generalized estimating equation model and outputs regression coefficient estimates; a risk prediction module that outputs individual risk probabilities and risk stratification results; and a decision support module that generates individual hearing protection recommendations; the present application processes follow-up data of workers exposed to occupational noise by constructing a generalized estimating equation model, thereby improving the accuracy and stability of language frequency hearing loss risk prediction, providing a basis for developing personalized hearing protection programs and recommending suitable hearing protection devices, and helping to realize the transition from passive treatment to active prevention of occupational hearing loss.