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Multi-factor model optimization method for gene regulatory network

A gene regulation network and optimization method technology, applied in the field of multi-factor model optimization of gene regulation network, can solve the problems of complex calculation and inaccurate models, and achieve the effect of accurate parameters and simple parameter adjustment process

Pending Publication Date: 2021-10-08
DALIAN MARITIME UNIVERSITY
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Problems solved by technology

[0004] The invention provides a multi-factor model optimization method of gene regulatory network to overcome technical problems such as inaccurate models and complicated calculations

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  • Multi-factor model optimization method for gene regulatory network
  • Multi-factor model optimization method for gene regulatory network
  • Multi-factor model optimization method for gene regulatory network

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Embodiment Construction

[0035] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments It is a part of embodiments of the present invention, but not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

[0036] This embodiment provides a multi-factor model optimization method of a gene regulatory network, such as figure 1 , the specific steps are:

[0037] Step 1, constructing the gene expression data differential equation model;

[0038] when f i When is a nonlinear function, the formula for constructing the gene expression data differential e...

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Abstract

The invention discloses a multi-factor model optimization method for a gene regulatory network, which comprises the following steps: inferring the gene regulatory network by utilizing a nonlinear differential equation model and a differential operation method of a gene and combining a machine learning algorithm, and optimizing a key parameter attenuation rate based on a multi-objective optimization idea and a genetic algorithm. According to the invention, the effects of noise, attenuation rate and time delay are introduced on the basis of a traditional differential equation model, a nonlinear regulation and control function is trained based on a machine learning algorithm, and the nonlinear dynamic process of gene expression can be better simulated; the algorithm is used for optimizing the attenuation rate and time delay of important parameters influencing gene expression, and then a more accurate and efficient mathematical model is constructed to deduce the gene regulation network from gene expression data.

Description

technical field [0001] The invention relates to the field of gene regulation network, in particular to a multi-factor model optimization method of gene regulation network. Background technique [0002] The development of next-generation high-throughput sequencing technology has obtained a wealth of gene expression data, which hides the dynamic process of genes and the interaction relationship between each other. Understanding the dynamic process of genes and the regulatory relationship between each other helps to understand The regulatory mechanism of organisms, from the perspective of the system, understands and understands the transmission of biological genetic signals, cell division and other activity rules. The complex interaction relationship between these genes can be abstracted into a network structure called gene regulatory network. By correctly inferring the gene regulatory network, the regulatory relationship between genes can be discovered, and the key regulatory ...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06N3/00G06N3/04G06N3/08
CPCG06N3/006G06N3/08G06N3/045G06F18/214Y02T10/40
Inventor 马宝山董恒宫弈杨博雅蒋宪思刘昱含
Owner DALIAN MARITIME UNIVERSITY
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