Molecular regulation relationship prediction method and system based on knowledge graph

Abstract

The invention belongs to the field of graph theory learning and deep learning analysis, and particularly relates to a molecular regulation relation prediction method and system based on a knowledge graph, and the method comprises the following steps: (1), preparing a training data set; (2) constructing and training a directed graph model; (3) constructing and training a neural network; and (4) performing actual prediction. According to the method, the overall process design of the prediction method is improved, and the method comprises the following steps: firstly, carrying out optimal feature coding on a directed regulation relationship and each regulation molecule node (namely each regulation molecule instance) in a directed graph in combination with graph learning; the prediction capability of the directed graph is further enhanced by using a deep neural network framework, and the prediction accuracy of directed regulation and control is improved after iterative training.

Description

technical field [0001] The present invention belongs to the field of graph theory learning and deep learning analysis, and more specifically, relates to a method and system for predicting molecular regulatory relationships based on knowledge graphs, using neural network learning of knowledge graphs and deep learning framework training to achieve the goal of constructing The purpose of reasonable molecular prediction based on prior knowledge is to serve as a guide for subsequent omics data analysis and experimental design. Background technique [0002] There are many existing intermolecular interaction relationships, and most of the intermolecular interaction networks constructed by research are undirected networks. Therefore, various analysis and inferences based on undirected graphs cannot effectively mine the order information of intermolecular interactions. For example, in the construction of a molecular regulatory network, if an undirected network is used for constructio...

AI Technical Summary

Problems solved by technology

[0002] There are many existing intermolecular interaction relationships, and most of the intermolecular interaction networks constructed by research are undirected networks, so various analysis and deduction based on undirected graphs cannot effectively mine the order information of intermolecular interactions

For example, in the construction of a molecular regulatory network, if an undirected network is used for construction and modeling, the user can only obtain the interaction relationship between molecules, that is, only know that there is a regulatory relationship between two molecules, but the specific regulatory process is unknown. Therefore, when the trained model predicts, there will be cases where the prediction result is opposite to the real result but the prediction score is still high.

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