Microbial-disease relation prediction method based on similarity and double random walk

Abstract

The invention discloses a microbial-disease relation prediction method based on the similarity and the double random walk. According to the method, firstly, the disease function similarity is constructed by utilizing the disease gene relation and the gene function similarity information. Secondly, the disease Gaussian kernel similarity is constructed according to the known microbial disease relation. On the basis of the disease function similarity and the Gaussian kernel similarity, a final similarity of diseases is integrated. The similarity of microorganisms is derived from the Gaussian kernel similarity of the known microbial disease relation. The microorganism similarity information, the disease similarity information and the known microbial disease relation information are integratedinto a double-layer heterogeneous network. In this way, the microbial disease relation prediction is carried out in the heterogeneous network by utilizing the double-random walk method. According to the method, the relation between microbial diseases can be predicted. The method provides a foundation basis for biological experiments. By adopting the method, the manpower and the material cost are saved.

Description

technical field [0001] The invention belongs to the field of systems biology and relates to a microorganism-disease relationship prediction method based on similarity and double random walks. Background technique [0002] More and more studies have shown that microbes play a very important role in many complex human diseases. With the rapid development of the current next-generation DNA sequencing technology, the discovery of the relationship between microorganisms and diseases in the human body has been promoted, such as microbiota and various cancer diseases, cardiovascular diseases, metabolic syndrome (such as obesity and diabetes), central nervous system Nervous system diseases and autoinflammatory diseases, etc. These studies not only contribute to the understanding of the disease mechanism, but also contribute to the development of new treatment and diagnosis schemes for the disease. For example, fecal microbiota transplantation has been identified as a safe and effe...

AI Technical Summary

Problems solved by technology

[0004] Constrained by the efficiency of biological experiment verification, insufficient attention and progress in predicting the relationship between microorganisms and diseases through computational models, and the need for further improvement in the prediction results, the current systematic understanding of the relationship between microorganisms and diseases is still limited

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