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A method for predicting association relationship between metabolites and diseases based on a KATZ model

A technology of association relationship and prediction method, which is applied in the field of bioinformatics, can solve problems such as too large deviations, and achieve the effect of improving prediction performance

Pending Publication Date: 2019-12-24
SHAANXI NORMAL UNIV
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Problems solved by technology

If the deviation between the result and the hypothesis is too large, or the experimental results prove that the hypothesis does not make much sense, the experimenter will bear certain losses such as time, manpower, funds, etc.

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  • A method for predicting association relationship between metabolites and diseases based on a KATZ model
  • A method for predicting association relationship between metabolites and diseases based on a KATZ model
  • A method for predicting association relationship between metabolites and diseases based on a KATZ model

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Embodiment

[0089] Taking the metabolite and disease network as an example, the steps of a method for predicting the relationship between metabolites and diseases based on the KATZ model are as follows:

[0090] In this embodiment, the relevant metabolic data collected from the latest version of the HMDB database were screened and deduplicated, and 4537 known relationships were obtained, including 216 diseases and 2262 metabolites. Experiment platform is Windows 10 operating system, Intel (R) Core (TM) i5-8500CPU@3.00GHz processor, 8GB physical memory, realizes the method of the present invention with pycharm software.

[0091] 1. Transform the known relationship between metabolites and diseases extracted from the relevant metabolic data of the latest version of the HMDB database into known metabolites and disease networks:

[0092] First, the known relationship between metabolites and diseases is transformed into an adjacency matrix M(nd*nm), where nd represents the total number of disease...

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Abstract

The invention discloses a method for predicting association relationship between metabolites and diseases based on a KATZ model. The method is characterized by converting known metabolite and diseaserelationship into a relationship network, calculating corresponding disease and metabolite similarity, constructing disease and metabolite similarity networks, predicting the relationship between metabolites and diseases through the KATZ model, verifying the accuracy of the predicted relationship through several cross validation methods, and further verifying the feasibility of the method throughcase analysis. The method can predict a new relationship between the metabolites and diseases, and a part of the relationship is verified by literatures and is not recorded by a database temporarily;verification results show that AUC index performance is excellent; and compared with other key protein recognition methods, the method improves the accuracy in mining potential relations of the prediction method by fusing the biological characteristics (semantic similarity) and topological characteristics (Gaussian kernel similarity) of the diseases.

Description

technical field [0001] The invention belongs to the field of biological information, and in particular relates to a method for predicting the relationship between metabolites and diseases based on a KATZ model. Background technique [0002] Metabolism is a collective term for a series of ordered chemical reactions that play a vital role in maintaining human life, such as the growth and reproduction of organisms and the body's response to the external environment. A large number of studies and experiments have shown that the concentration of certain metabolites in the body is different when the body is sick compared with the normal body. Therefore, the relevant metabolite-disease association has become an important basis for doctors to diagnose and treat patients. There are many common examples in life, such as diabetes. Diabetes may automatically come to mind when people think of blood sugar. The reason for this phenomenon is because the blood sugar concentration in the b...

Claims

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Application Information

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IPC IPC(8): G16H50/20G16H50/70
CPCG16H50/20G16H50/70
Inventor 雷秀娟张程樊春燕
Owner SHAANXI NORMAL UNIV
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