Maximum grade predication method based on binding Boolean network

A scoring prediction, Boolean network technology, applied in special data processing applications, instruments, electrical and digital data processing, etc., can solve problems such as high data quality requirements, high noise in biological data, etc., to achieve accurate network structure and strong robustness Effect
CN103646159AInactive Publication Date: 2014-03-19WENZHOU UNIVERSITY
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Patent Information

Authority / Receiving Office
CN · China
Current Assignee / Owner
WENZHOU UNIVERSITY
Publication Date
2014-03-19
Estimated Expiration
Not applicable · inactive patent

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Abstract

The invention relates to a maximum grade predication method based on a binding Boolean network. The maximum grade predication method comprises the following steps that 1, relative mutual information is calculated to determine a candidate predicated gene set; 2, father genes used as target genes are selected from the predicated gene set obtained in the step 1 through the maximum grade predication method. The maximum grade predication method is suitable for predicating the relation of multiple variables based on small-sample data, has stronger robustness on noise, and is more suitable for predicating biological data under a real environment, the predicated network structure is more accurate and detailed, and the right number and the directivity of regulating and control relations and positive and negative regulating and control relations are predicated.
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Description

technical field

[0001] The invention relates to a method for predicting a gene regulation network, in particular to a method for predicting the maximum score of a gene regulation network by utilizing the constrained Boolean network characteristics. Background technique

[0002] An important goal of systems biology research is to describe the molecular mechanisms that regulate specific cellular behaviors and processes. There are many models describing the gene regulation network, for example: Bayesian network and dynamic Bayesian network provide a model that can clarify the dependence relationship between genes; Boolean network and probabilistic Boolean network It is a method for studying the function of a system in terms of state behavior; the differential equation is a continuous model, which can describe the detailed biochemical relationship between genes. These models are uniformly used to study biological phenomena (cell cycle) and diseases (cancer). Therefore, reveali...

Claims

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