A Gene Network Model Inference Method Based On The Interaction Of Functional Proteins

Nature and human society do not look as messy,random,or irregular as one might imagine.For some complex things,they often contain a large number of internal components,and the various components often have unique properties.There are also non-random relations between these unique properties,which highlight a variety of properties.As a kind of complex system,the organism contains numerous kinds of biomolecules in the cell,and its biological components are also diversified and diversified.There are countless cells in living organisms that can coordinate with one another,complicating the behavior of biology as a whole.In the process of describing biological objects,this complex network can be selected to make the whole description of biological objects more natural first.This biological network is shown by the interrelationship among the components.In this paper,the gene network model of functional protein interaction is constructed,and the corresponding model is constructed to create functional annotation analysis method of related gene sets.In this project,the problem to be solved is to study the relationship between functional protein interactions.In the research phase of this protein interaction,we consulted a large number of literatures to provide more substantial research data for data mining.In the study of cancer-related genes,the data of gene mutation and gene expression obtained from the experimental data can provide theoretical basis for finding the optimal genes.In the stage of selecting the optimal genes,we make full use of the characteristics of biological networks,such as protein interaction networks and co-expression networks.The more common biological network mainly includes protein interaction,neural network method and this basic linear review method model,which can be used to see the corresponding key genes and the interaction of key genes.Finally,the positive effect of this binding method on promoting protein interaction was further evaluated.