A Microbe-Disease Relationship Prediction Method Based on Similarity and Low-Rank Matrix Filling

Abstract

The invention discloses a microbe-disease relation predicting method based on similarity and low-rank matrix filling. Firstly a final disease similarity is obtained in a mean value integrating mannerof disease Gaussian similarity, disease symptom similarity and disease similarity. The Gaussian core similarity of the microbe is calculated by means of a known microbe-disease association relation. Furthermore adjustment processing is performed on the Gaussian core similarity according to parasitic tissue information of the microbe, and a final microbe similarity is obtained. Finally a microbe similarity network is connected with a disease similarity network through the known microbe-disease association relation, thereby constructing a microbe-and-disease heterogeneous network. According to the association relation matrix of the heterogeneous network, a low-rank matrix filling method is used for predicting the microbe-disease association relation. Furthermore before filling, an association relation initializing processing process is added for improving predicting precision. The microbe-disease relation predicting method can effectively predict the microbe-disease association relation.

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 low-rank matrix filling. Background technique [0002] With the development of high-throughput sequencing technology and microbiome, many studies have shown that there is an inseparable relationship between microorganisms and human diseases. Microorganisms affect human diseases and health by participating in important life processes such as metabolism and apoptosis, such as cardiovascular diseases, autoinflammatory diseases and cancers. Currently, there are four main types of microbial-disease associations. One is that changes in microorganisms are the direct cause of diseases; the other is that there is no direct relationship between microorganisms and diseases, but there is a common third-party factor; the third is that diseases can affect changes in microorganisms so that they can be used as a disease d...

AI Technical Summary

Problems solved by technology

[0005] The technical problem to be solved by the present invention is to propose a microbe based on similarity and low-rank matrix filling for the problem of insufficient utilization of microbe and disease-related biological information in the current method for predicting the relationship between microbes and diseases through computational models. -Disease relationship prediction method, which integrates the mean value of disease Gaussian kernel, representational similarity and functional similarity to obtain the final disease similarity network. Microbial similarity is obtained by adjusting the parasitic tissue information on the basis of Gaussian kernel similarity

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