miRNA biomarker identification method and system based on stack auto-encoder

A biomarker and self-encoder technology, applied in neural learning methods, biological neural network models, physical realization, etc., can solve problems such as the inability to make full use of the information of unlabeled samples, and achieve the effect of overcoming limitations

Pending Publication Date: 2021-12-03
CHINA UNIV OF MINING & TECH
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AI Technical Summary

Problems solved by technology

Most of the disease and miRNA association prediction models based on machine learning algorithms cannot make full use of the information of unlabeled samples

Method used

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  • miRNA biomarker identification method and system based on stack auto-encoder
  • miRNA biomarker identification method and system based on stack auto-encoder
  • miRNA biomarker identification method and system based on stack auto-encoder

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Embodiment Construction

[0074] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0075] A method for identifying miRNA biomarkers based on stacked autoencoders, including:

[0076] S1: Collect the association information of known human complex diseases and miRNAs from the database and store the association information in matrix form:

[0077] From the HMDD v2.0 database, 5430 pairs of human complex diseases and miRNA associations are obtained, including 383 human complex diseases and 495 miRNAs; the association information is stored in the...

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Abstract

The invention discloses a miRNA biomarker identification method and system based on a stack auto-encoder. The method comprises the following steps: collecting known human complex diseases and associated information of miRNA from a database, and storing the associated information in a matrix form; calculating the similarity between the diseases; calculating the similarity between the miRNAs; constructing a feature vector of the human complex disease-miRNA biomarker pair; building a stack auto-encoder neural network and pre-training neural network parameters; performing fine tuning on the stack auto-encoder neural network by using a labeled complex disease-miRNA sample; and identifying the miRNA biomarker related to the human complex disease based on the trained stack auto-encoder neural network. According to the method, the information of the labeled samples and the information of the unlabeled samples are fully utilized, and the limitation that the information of the unlabeled samples cannot be fully utilized in a traditional machine learning method is overcome.

Description

technical field [0001] The invention relates to the field of bioinformatics, in particular to a method and system for identifying miRNA biomarkers based on a stacked autoencoder. Background technique [0002] MicroRNA (miRNA) is a class of endogenous non-coding RNA with a length of about 22 nucleotides, which plays a variety of important regulatory roles in cells. Studies have shown that miRNAs are involved in many important life processes, including early cell development, cell proliferation, cell apoptosis, fat metabolism, and cell differentiation. In addition, a large number of studies have shown that miRNA plays an important role in the occurrence and development of human diseases. For example, high expression of miR-155 in humans can promote T cell-mediated Helicobacter pylori production, which in turn leads to chronic gastritis and colitis. In addition, the expression level of miR-203 was positively correlated with the degree of foot ulcers in diabetic patients, whic...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/04G06N3/06G06N3/08G16H50/20G06F40/30G06F17/16
CPCG06N3/04G06N3/061G06N3/08G06F17/16G06F40/30G16H50/20
Inventor 陈兴王纯纯李天皓
Owner CHINA UNIV OF MINING & TECH
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