Method and system for predicting and screening mother source transmission key bacteria based on neural network

By constructing an attention mechanism model based on neural network, analyzing mother-son microbial data and predicting key bacteria from the parent source, the problem of difficult to accurately identify and predict key bacteria in the existing technology is solved, and efficient and accurate strain screening and association analysis are achieved.

CN119694401AActive Publication Date: 2025-03-25HUNAN AGRI UNIV
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Patent Information

Application Number
CN202411743239.8
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-11-29
Publication Date
2025-03-25
Estimated Expiration
2044-11-29

AI Technical Summary

Technical Problem

The prior art is difficult to accurately identify and predict the key bacterial genera in parental transmission and their impact on the growth and development of offspring. Especially when facing large-scale high-dimensional microbiome data, it is susceptible to noise interference, resulting in unstable prediction results and low accuracy.

Method used

Using a neural network-based method, a neural network model of attention mechanism was constructed, the mother-son microbial sequencing data were analyzed, the key bacteria of the parent were predicted, and the strains that affected the key phenotype were screened out through the directional culture group.

Benefits of technology

It realizes accurate identification and screening of key bacteria delivered by the parent source, improves prediction efficiency and accuracy, can effectively correlate relevant phenotypes, and provides scientific microbial resource development tools.

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Abstract

The invention belongs to the technical field of computers, and discloses a neural network-based parent source transmission key bacterium prediction and screening method, which comprises the following steps of: 1, acquiring parent and child microorganism data to form a data set, and dividing the data set into a training set and a test set; 2, constructing a neural network model based on an attention mechanism, and training and adjusting the neural network model by adopting the training set and the test set to obtain a neural network model for predicting piglet microorganism data according to the input sow microorganism data; and step 3, carrying out a feature ablation experiment on the neural network model trained in the step 2 in a mode of removing bacterial genus in the sow microbial data in the data set one by one to obtain key bacteria which have obvious influence on maternal source transmission. According to the method, an advanced deep learning algorithm in the field of artificial intelligence is utilized, and a neural network model capable of predicting mother source key bacteria is constructed by analyzing mother and child microorganism sequencing data. The model not only can extract bacterial genus having key influence on growth performance from large-scale microbial communities, but also can screen out bacterial strains having influence on key phenotypes through directional culture groups of predicted bacteria. Meanwhile, the invention further provides a system based on the method.
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