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CircRNA-disease association predicating method based on network model

A prediction method and network model technology, applied in the interdisciplinary fields of bioinformatics and artificial intelligence, can solve problems such as time-consuming, waste of manpower and financial resources, and achieve the effects of short time-consuming, high prediction accuracy and cost reduction

Pending Publication Date: 2019-04-30
GUILIN UNIV OF ELECTRONIC TECH
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Problems solved by technology

However, because traditional biological experiment methods to identify circRNA-disease associations not only need a lot of time, but also waste manpower and financial resources, there is an urgent need for a quick method to identify circRNA-disease associations, thereby reducing costs and guiding biological Medical Research
However, so far, there is no good way to solve this problem

Method used

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  • CircRNA-disease association predicating method based on network model
  • CircRNA-disease association predicating method based on network model
  • CircRNA-disease association predicating method based on network model

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Embodiment

[0036] refer to figure 1 , a network model-based circRNA-disease association prediction method, including the following steps:

[0037] 1) Obtain the circRNA-disease association data set and construct the adjacency matrix A about the circRNA-disease association: Obtain the circRNA-disease association data confirmed by biological experiments from the circRNADisease database. Due to the small amount of chicken and mouse species association data, it is not representative , we deleted the circRNA-disease association data of chicken and mouse species, and only kept the circRNA-disease association data of human species, and finally obtained 239 pairs of different circRNA and disease association data, involving 34 types of diseases and 223 types of circRNAs. Define D={d(1), d(2), d(3),...,d(nd)} to record the set of nd diseases, C={c(1), c(2), c (3),..., c(nc)} to record the collection of nc circRNAs, and build an adjacency matrix A nd×nc Indicates the relationship between circRNA ...

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Abstract

The invention discloses a circRAN-disease association predicating method based on a network model. The method is characterized by comprises the following steps of 1), acquiring a circRNA-disease association data set, and constructing an adjacent matrix A which is related with circRNA-disease association; 2), constructing a circRNA Gaussian interaction attribute kernel similarity matrix KC; 3), constructing a disease Gaussian interaction attribute kernel similarity matrix KD; and 4), performing circRNA-disease association matrix according to a network consistency projection model. The method has low cost and can improve circRNA-disease association prediction precision.

Description

technical field [0001] The invention relates to the interdisciplinary field of bioinformatics and artificial intelligence, in particular to a network model-based circRNA-disease association prediction method. Background technique [0002] It is well known that genetic information is stored in protein-coding genes, which is known as the central dogma of molecular biology. For this reason, RNA was for a considerable time only considered as an intermediary between a DNA sequence and the protein it encodes. Recent studies have shown that protein-coding genes make up only a small fraction (approximately 1.5%) of the human genome. In other words, over 98% of the human genome does not encode protein sequences. In particular, it has been observed that the proportion of non-protein coding sequences increases with the complexity of the organism. These facts challenge the conventional view of RNA mentioned earlier. Furthermore, accumulating evidence indicates that non-coding RNAs (...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G16H50/80G16B20/00G16B40/00
CPCG16H50/80
Inventor 樊永显朱庆祺张向文张龙
Owner GUILIN UNIV OF ELECTRONIC TECH
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