3 results about "Diverse population" patented technology

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.

The invention discloses a population prediction method based on multi-source data fusion. According to the population prediction method provided by the invention, by fusing multi-source data and improving a queue element method framework, high-precision prediction of the total population and age and gender structures in the future on fine geographic scales such as streets and communities is realized. The method comprises the following steps: firstly, performing clustering analysis by utilizing static characteristic data reflecting geographic unit attributes and planning, and dividing units into categories with different population evolution rules; furthermore, for each category, historical migration parameters are accurately quantified by fusing demographic statistics and individual permanent residence change data, and an exclusive migration prediction model is trained based on dynamic indexes, population feature factors and policy factors which change along with time. And finally, combining future parameter deduction in iterative prediction, and automatically calling a corresponding model to calculate mechanical growth. According to the embodiment, the accuracy and integrity of the prediction result are improved.

The present application relates to the technical field of machine learning and population prediction, and particularly relates to a population prediction method and system based on adaptive ridge regression and multi-model stacking; firstly, the original population data is preprocessed by anomaly value repair, first-order difference, standardization and PCA dimension reduction to generate time series features; then, three models of multilayer perceptron, random forest and linear regression are constructed, cross-validation and grid search are used to optimize the hyperparameters and complete the training; then, through the hierarchical stacking strategy, the prediction output of the basic model is input into the ridge regression sub-model as a new feature; finally, based on the performance of each basic model in different population characteristic prediction tasks, the weight proportion is dynamically adjusted through the regularization parameter, and the accurate prediction of multi-dimensional population characteristics is realized. The present application effectively integrates the advantages of each model, improves the prediction accuracy and stability of indicators such as total population, gender structure, urban-rural flow, and provides a scientific basis for population strategy formulation, public resource planning and risk warning.