Mining And Analysis Of The Relationship Network Of Rice Blast Pathogenic Factors Based On Machine Learning

Rice is one of the most important food in the world.It has high economic value.Rice blast is one of the three major rice diseases in China,and it is a very serious fungal disease.Rice blast harms rice and infects other crops such as wheat,barley,and millet,causing serious damage to global crop yields.It is a very important plant pathogen.Although some progress has been made in fungal-plant interactions,it is still challenging to understand the complex molecular mechanism.With the continuous development of highthroughput sequencing technology,researchers can integrate and analyze the association between biological features and diseases through multi-omics data,present complex biomolecular interaction processes,and analyze the relationship between molecular markers.Starting from multi-omics data,this paper combines genomics and transcriptomics to construct a rice blast fungus-rice interaction network to mine and analyze the pathogenic factors of rice blast fungus.In this paper,a rice blast fungus-rice interaction prediction model based on WGCNA--Rice Blast Hub Factor Prediction（RBHFP）--is independently constructed to identify unknown pathogenic factors the key sRNA of rice blast fungus.First,obtain rice genomics data and rice blast fungus transcriptomic data obtained by high-throughput sequencing.In terms of genomics,the WGCNA model was used to construct a weighted co-expression network for rice genes,and 110 key rice genes expressed with rice blast were found.In terms of transcriptomics,366 rice blast sRNAs were found out through differential expression analysis and expression rate.Then,the rice blast fungus sRNA was targeted to the rice gene to obtain 741 rice blast fungus-rice interaction pairs,and the rice blast fungus sRNA-rice m RNA heterogeneous network was constructed.Next,the rice blast fungus nodes in the network were analyzed to find the hub sRNA.Finally,the functional enrichment analysis of rice genes targeted by sRNA of rice blast was carried out,and some GO（Gene Ontology）and KEGG Pathways were obtained.The enrichment pathways were all involved in the process of rice blast fungus infecting rice,and were closely related to rice gene expression and molecular synthesis,metabolism,etc.The prediction model for key factors of rice blast constructed in this paper integrates genomics and transcriptomic data,and can identify the core genes and key factors of rice blast in the process of rice blast-rice interaction at multi-omics levels sRNA,and the key pathogenic factors of the obtained rice blast fungus were verified through enriched pathways.Therefore,this model is applicable to predicting pathogenic factors in other fungi-plant interactions to a certain extent,laying a theoretical foundation for the study of other fungi-plant interactions and revealing the molecular roles in the process of fungal-plant interactions.The mechanism supports the mining of pathogenic factors of plant-fungal complex diseases and provides new ideas and research directions.