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Feature gene selecting and cancer classifying method

A technology of characteristic genes and classification methods, applied in the field of biomedical data analysis, can solve the problem of not taking into account the interaction network between genes and genes, and achieve the effect of improving accuracy

Active Publication Date: 2019-04-23
SHAOGUAN COLLEGE
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Problems solved by technology

[0005] The embodiment of the present invention provides a feature gene selection and cancer classification method, which can solve the technical problem that the traditional SCAD method does not take the interaction network between genes into consideration, thereby effectively improving the accuracy of feature gene selection and cancer classification , which in turn facilitates the study of disease

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specific Embodiment approach

[0070] As a kind of specific implementation manner of the embodiment of the present invention, described setting up the solving model of SCAD-Net, wherein the expression of the solving model of described SCAD-Net is:

[0071]

[0072] in,

[0073] Assuming that gene i and gene k are connected in the biological regulatory network, then w ik =1 or a real number from 0 to 1, on the contrary if there is no connection then w ik = 0; d i and d k is the degree of gene i and gene k in the biological regulatory network (in-degree + out-degree); λ 1 and lambda 2 are hyperparameters for adjusting model sparsity and model smoothness, respectively. α is a constant greater than 2.7.

[0074] In the embodiment of the present invention, the selection of hyperparameters is performed by cross-validation method.

[0075] As a specific implementation manner of the embodiments of the present invention, the SNL model is obtained by combining the solution model of the loss function and t...

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Abstract

The invention discloses a feature gene selecting and cancer classifying method which at least comprises the following steps of establishing a logistic regression model according to a super-parameter set and a to-be-processed gene data set; according to maximum likelihood estimation and logarithmic operation, expressing the logistic regression model as a loss function; establishing an SCAD-Net resolving model; according to the loss function and the SCAD-Net resolving model, obtaining an SNL model; calculating an iteration updating operator of the SCAD-Net; according to the iteration updating operator, calculating a gene regression coefficient of the SNL model through a coordinate gradient descent method; and according to the gene regression coefficient, performing feature gene selection andcancer classification. The feature gene selecting and cancer classifying method can effectively improve feature gene selecting and cancer classifying accuracy, thereby facilitating disease researching.

Description

technical field [0001] The invention relates to the technical field of biomedical data analysis, in particular to a feature gene selection and cancer classification method. Background technique [0002] Accurately classifying cancer and identifying its disease-associated biomarkers is of great significance for the clinical treatment of tumors. At present, microarray gene chip technology is an important genome data collection technology, and the gene expression profile data obtained by this technology have been widely used in the classification of tumor subtypes and the identification of biomarkers. [0003] However, due to the characteristics of high-dimensional small samples of gene expression profiles, that is, the number of samples collected is far smaller than the number of genes (also known as the small n, large p problem), the tumor prediction model established on the gene expression profile and The screened genes are very prone to overfitting and false positive probl...

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Application Information

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IPC IPC(8): G16B40/00G16B20/20G16B25/10
CPCY02A90/10
Inventor 黄海辉戴经国梁勇陈燕琴
Owner SHAOGUAN COLLEGE
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