Pulmonary arterial hypertension target mining verification simulation system integrating multiple omics data

By integrating multi-omics data into a pulmonary hypertension target mining and validation simulation system, the batch effect and single-algorithm overfitting problems in high-dimensional biomedical data processing have been solved, achieving highly specific identification and cross-scale analysis of pulmonary hypertension targets, and supporting the development of targeted drugs.

CN122455384APending Publication Date: 2026-07-24新疆第二医学院
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Patent Information

Application Number
CN202610568102.6
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-04-27
Publication Date
2026-07-24

AI Technical Summary

Technical Problem

Existing technologies suffer from batch effect interference and overfitting of single algorithms when processing high-dimensional biomedical data, resulting in low specificity for pulmonary hypertension target identification and a lack of cross-scale system analysis.

Method used

A simulation system for target discovery and validation in pulmonary hypertension using multi-omics data integration includes a data input layer, a differential analysis module, a weighted gene co-expression network analysis module, a protein interaction network analysis module, and a multi-machine learning ensemble screening module. It uses an empirical Bayesian framework to handle batch effects and combines multiple machine learning models for target feature screening and validation.

Benefits of technology

It improves the specificity and accuracy of pulmonary hypertension target identification, can accurately identify core functional modules, and constructs cross-scale biological analysis models to achieve targeted relocation of small molecule compounds.

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Abstract

The invention relates to the technical field of bioinformatics and medical data processing, and discloses a pulmonary arterial hypertension target mining verification simulation system integrating multi-omics data, which comprises a data input layer for receiving transcriptome data, executing batch correction by adopting a linear regression model based on an empirical Bayesian framework and outputting a standardized gene expression matrix; the difference analysis module extracts a difference expression gene set; the weighted gene co-expression network analysis module constructs a topological overlap matrix based on the soft threshold parameters, and outputs a core module gene set; the protein interaction network analysis module obtains an intersection of the two sets, and calls a molecular complex detection algorithm to output a target feature set with high connectivity; and the multi-machine learning integrated screening module performs independent dimension reduction extraction by adopting elastic network regression, support vector machine recursive feature elimination and a random forest model. According to the method, the overfitting deviation of a single algorithm is overcome, and the target spot identification accuracy is improved.
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