An auxiliary screening method for genetic metabolic diseases based on multi-domain fusion learning

Abstract

The invention discloses an auxiliary screening method for genetic metabolic diseases based on multi-domain fusion learning. The method projects the screening data nonlinearly into the latent space for representation, and establishes the constraint on the distribution difference of metabolites in different regions to realize multi- Unified modeling of regional screening data, the invention uses the nonlinear mapping of the neural network to represent the correlation between different metabolites; uses the multi-domain fusion technology in the main neural network to establish a unified model based on multi-regional screening data model; and the total amount of data is increased through data fusion, so that the model reduces the false positive rate while keeping the recall rate unchanged.

Description

technical field [0001] The invention belongs to the field of information technology applications, and relates to an auxiliary screening method for genetic metabolic diseases, in particular to an auxiliary screening method for genetic metabolic diseases based on multi-domain fusion learning. Background technique [0002] Hereditary metabolic diseases are a large class of genetic diseases with metabolic function defects. The mutated gene changes the original protein code and affects the synthesis of enzymes. Due to the lack of enzymes, biomolecules in some metabolic pathways cannot be effectively decomposed, and the concentration of metabolites upstream and downstream of the pathway is out of the normal range, resulting in abnormal symptoms in the body, such as mental retardation, developmental delay and epilepsy, and severe cases may even face death. Early diagnosis and early treatment can greatly improve the prognosis, improve the quality of life of patients, and reduce the...

AI Technical Summary

Problems solved by technology

[0003] The high false positive rate is a major shortcoming of existing screening methods, that is, most of the recalled suspected cases are actually healthy people, which brings unnecessary waste of medical resources

The reason is that the main problems causing the high false positive rate are concentrated in three aspects: (1) The threshold method sets a separate abnormal concentration cut-off value for each metabolite, and this linear method ignores the correlation between metabolites (2) There are certain differences in the concentration distribution of metabolites in the population of different regions, and it is difficult to integrate and analyze the screening data. Therefore, each screening center can only rely on the data samples in the region to establish cut-off value standard, and the reduction of statistical samples, especially positive cases, will reduce the screening accuracy of the threshold method; (3) The huge birth population in my country increases the work pressure of each pediatrician, and at the same time, qualifications, emotions, etc. will also become factors. Potential Factors Affecting Interpretation

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