Model identification method and system for multilayer Boolean network
A Boolean network and model identification technology, applied in the field of model identification, can solve the problems that the logical relationship of biological models cannot be identified by multi-layer Boolean network models, and the single-layer or multi-layer Boolean networks cannot be described in detail.
Pending Publication Date: 2021-06-01
HARBIN UNIV OF SCI & TECH
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[0004] In view of the above problems, the present invention proposes a model identification method and system of a multi-layer Boolean network to solve the problem that the existing single-layer or multi-layer Boolean network cannot describe the logical relationship between biological models in detail and cannot identify the multi-layer Boolean network. The problem of effective identification of network models
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[0169] Based on the method of the present invention, the model identification of whether pathogenic microRNAs and different cancers share pathogenesis is studied. MicroRNA is an important class of non-coding RNA, and its abnormality can lead to the occurrence and development of human diseases. Through the detection of cancer tissue chips, the expression of many microRNAs in cancer tissues and normal tissues is very different, thus confirming that microRNAs are closely related to the occurrence of cancer.
[0170] In order to study whether the pathogenesis is shared between different diseases, the information of those known microRNA dysregulation that can cause diseases in the miR2Disease database was used to describe the relationship between pathogenic microRNAs and cancer. We first establish a model of whether pathogenic microRNAs and different cancers share pathogenesis.
[0171] Such as Figure 4 As shown, the rectangular and circular patterns in the figure represent vari...
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The invention discloses a model identification method and system for a multi-layer Boolean network, belongs to the technical field of model identification, and is used for solving the problem that an existing single-layer or multi-layer Boolean network cannot describe a logic relationship between biological models in detail and cannot identify a multi-layer Boolean network model. The method is technically characterized by comprising the following steps: firstly, acquiring observation data and preprocessing the observation data; then establishing a multi-layer Boolean network model, wherein the multi-layer Boolean network comprises a plurality of single-layer Boolean networks and global state layers among the layers; and finally, identifying one or more model structures of the multi-layer Boolean network; furthermore, according to the data of the contradictory column, giving out the probability of system selection, so that the final model structure of the multi-layer Boolean network is determined according to the probability. According to the method, the model structure can be accurately identified, so that the logic relationship in the biological model can be described in detail. The method can be widely applied to the research of the gene regulation network.
Description
technical field [0001] The invention relates to the technical field of model identification, in particular to a model identification method and system for a multi-layer Boolean network. Background technique [0002] Gene expression is a complex process. Many biological processes require gene expression through gene regulatory networks. In recent years, there has been great interest in studying genetic regulatory networks, which play a very important role in understanding biological processes and effectively controlling interventions. Boolean network is a discrete system based on directed graph, and it is a relatively simple logic dynamical system. Boolean networks can simulate some complex biological system networks. In 1969, Kaufman proposed that Boolean networks can be used to describe gene regulatory networks. For the first time, "0" and "1" in logic operations represent the two states of gene suppression and expression. . Since the Boolean network model is relatively...
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IPC IPC(8): G16B5/10
CPCG16B5/10Y02A90/10
Inventor 李丽丽崔禹欣李鹏飞
Owner HARBIN UNIV OF SCI & TECH