CN-122024821-A - Method for solving nonlinear regulation network model stability solution

CN122024821ACN 122024821 ACN122024821 ACN 122024821ACN-122024821-A

Abstract

The invention relates to the technical field of biological information and discloses a method for solving a stable solution of a nonlinear regulation network model, which can be used for pharmacological networks, physiological networks, toxicological networks and pathological networks and comprises the steps of constructing a nonlinear regulation network deep learning model, performing independent training on the same data set for multiple times by using the nonlinear regulation network deep learning model, storing a group of solutions in each training, wherein each group of solutions comprises all connection weights and node biases in the network to obtain a common solution set, screening the common solution set to obtain a high-quality solution set, respectively calculating corresponding average values of all weight parameters and bias parameters in the high-quality solution set to obtain corresponding fusion weights and fusion biases, introducing the fusion weights and the fusion biases into an original network structure, performing forward reasoning on a test set, and evaluating the prediction performance and stability of the fusion weights and the fusion biases.

Inventors

ZHENG GUANG
JIA FENG
LI ZHICHENG

Assignees

兰州大学

Dates

Publication Date: 20260512
Application Date: 20260130

Claims (8)

1. A method for solving a nonlinear regulation network model stabilization solution is characterized by comprising the following steps: Constructing a nonlinear regulation network deep learning model; Performing independent training on the same data set for multiple times by using a nonlinear regulation network deep learning model, wherein each training stores a group of solutions, and each group of solutions comprises all connection weights and node biases in a network to obtain a common solution set; Screening from the common solution set to obtain a high-quality solution set; Respectively calculating corresponding average values of all weight parameters and bias parameters in the high-quality solution set to obtain corresponding fusion weights and fusion biases; And importing the fusion weight and the fusion bias into an original network structure, performing forward reasoning on the test set, and evaluating the prediction performance and stability of the test set.
2. The method for solving the stability problem of the nonlinear regulation network model according to claim 1 is characterized in that the method for obtaining the high-quality solution set by screening from the common solution set comprises the steps of clustering all solutions in the common solution set by adopting a clustering method, and selecting a clustering center as the high-quality solution set.
3. The method for solving the stability problem of the nonlinear regulation network model according to claim 1, wherein the method for obtaining the quality solution set by screening from the common solution set comprises the steps of saving the loss value of each group of solutions during training, sorting the groups of solutions from small to large according to the loss value, and selecting the first N groups as the quality solution set.
4. The method for solving the stable solution of the nonlinear regulation network model according to claim 2 or 3, wherein the method for obtaining the corresponding fusion weight and the fusion bias by respectively calculating the corresponding average value of all weight parameters and bias parameters in the high-quality solution set comprises the steps of calculating the arithmetic average value of all weight parameters in the high-quality solution set to obtain the fusion weight, and calculating the arithmetic average value of all bias parameters in the high-quality solution set to obtain the fusion bias.
5. The method for solving the stable solution of the nonlinear regulation network model according to claim 2 or 3, wherein the method for calculating the corresponding average value of all weight parameters and bias parameters in the high-quality solution set and obtaining the corresponding fusion weight and fusion bias comprises the steps of taking the inverse of the loss value corresponding to each group of solutions in the high-quality solution set as the weight of the corresponding weight parameter and bias parameter respectively; calculating the weighted average value of all weight parameters in the high-quality solution set to obtain a fusion weight; and calculating the weighted average value of all bias parameters in the high-quality solution set to obtain the fusion bias.
6. A system for solving a stabilizing solution of a quantitative regulatory model of a nonlinear regulatory network, for implementing a method for solving a stabilizing solution of a nonlinear regulatory network model according to any one of claims 1-5, comprising: The model construction module is used for constructing a nonlinear regulation network deep learning model; The model training module is used for performing independent training on the same data set for a plurality of times by using a nonlinear regulation and control network deep learning model, and storing a group of solutions for each training, wherein each group of solutions comprises all connection weights and node offsets in a network to obtain a common solution set; The high-quality solution screening module is used for screening from the common solution set to obtain a high-quality solution set; The high-quality solution fusion module is used for respectively calculating corresponding average values of all weight parameters and bias parameters in the high-quality solution set to obtain corresponding fusion weights and fusion biases; And the fusion quality solution verification module is used for importing the fusion weight and the fusion bias into the original network structure, performing forward reasoning on the test set, and evaluating the prediction performance and stability of the test set.
7. An electronic device comprising a memory and a processor, the memory configured to store a computer program, the processor configured to execute the computer program to cause the electronic device to perform a method of solving for a nonlinear regulatory network model stability solution as defined in any one of claims 1 to 5.
8. A computer readable storage medium, characterized in that it stores a computer program, which when executed by a processor implements a method of solving for a stability solution of a nonlinear regulatory network model according to any one of claims 1 to 5.

Description

Method for solving nonlinear regulation network model stability solution Technical Field The invention relates to the technical field of biological information, in particular to a method for solving a nonlinear regulation network model stability solution. Background The signal path and disease control network have important scientific research value in the aspects of health and biomedical research. At present, research at home and abroad is mostly qualitative network structure discussion and research on a small amount of regulation and control relations. The Chinese patent discloses a method for solving quantitative regulation information of a pharmacological network, wherein the method is disclosed in patent number CN202310852405.7, and a plurality of groups of real solutions of the regulation network are obtained by using a fully-connected neural network to simulate a real in-vivo gene regulation model. However, due to the reasons of initial weight, optimization path, connection constraint and the like in the deep learning training process, real solutions obtained by multiple training are distributed and dispersed in a parameter space, so that the model output stability is insufficient. Disclosure of Invention The invention aims to provide a method for solving a nonlinear regulation network model stability solution, which effectively improves the reliability and the practicability of the solution. The invention is realized in the following way: a method for solving a nonlinear regulation network model stabilization solution comprises the following steps: Constructing a nonlinear regulation network deep learning model; Performing independent training on the same data set for multiple times by using a nonlinear regulation network deep learning model, wherein each training stores a group of solutions, and each group of solutions comprises all connection weights and node biases in a network to obtain a common solution set; Screening from the common solution set to obtain a high-quality solution set; Respectively calculating corresponding average values of all weight parameters and bias parameters in the high-quality solution set to obtain corresponding fusion weights and fusion biases; And importing the fusion weight and the fusion bias into an original network structure, performing forward reasoning on the test set, and evaluating the prediction performance and stability of the test set. Further, the method for screening and obtaining the high-quality solution set from the common solution set comprises the steps of clustering all solutions in the common solution set by adopting a clustering method, and selecting a clustering center as the high-quality solution set. Further, the method for obtaining the high-quality solution set from the common solution set by screening comprises the steps of saving the loss value of each group of solutions during training, sequencing the solutions of each group according to the small-to-large loss value, and selecting the first N groups as the high-quality solution set; Further, the method for obtaining the corresponding fusion weight and fusion bias by respectively calculating the corresponding average value of all weight parameters and bias parameters in the high-quality solution set comprises the steps of calculating the arithmetic average value of all weight parameters in the high-quality solution set to obtain the fusion weight, and calculating the arithmetic average value of all bias parameters in the high-quality solution set to obtain the fusion bias. Further, calculating corresponding average values of all weight parameters and bias parameters in the high-quality solution set respectively to obtain corresponding fusion weights and fusion biases, wherein the inverse of loss values corresponding to each group of solutions in the high-quality solution set are used as weights of the corresponding weight parameters and bias parameters respectively; calculating the weighted average value of all weight parameters in the high-quality solution set to obtain a fusion weight; and calculating the weighted average value of all bias parameters in the high-quality solution set to obtain the fusion bias. A system for solving a stabilizing solution of a quantitative regulation model of a nonlinear regulation network, which is used for realizing any one of the above methods for solving the stabilizing solution of the nonlinear regulation network model, and is characterized by comprising the following steps: The model construction module is used for constructing a nonlinear regulation network deep learning model; The model training module is used for performing independent training on the same data set for a plurality of times by using a nonlinear regulation and control network deep learning model, and storing a group of solutions for each training, wherein each group of solutions comprises all connection weights and node offsets in a network to obtain a common solution set; The high-