A Gene Network Inference Method Based on Modular Recognition

Abstract

The invention discloses a gene network reasoning method based on modular identification. The method takes the expression information of n genes as the training set, and each gene has m samples; uses the ICA-FDR algorithm to identify the gene modules and divides the n genes into different gene modules; the regulation relationship within the gene module The algorithm based on gradient boosting tree is used for inference, and the regulatory relationship between gene modules is inferred by the algorithm based on sparse regression, and the correlation score of each gene pair is obtained; for each gene module and inter-module reasoning The obtained correlation scores were respectively normalized and merged, and sorted in descending order to obtain the final gene regulatory network. The invention provides a seamless fusion framework for gene module identification and gene network reasoning, improves the accuracy of gene module identification, and increases the interpretability of gene regulation network functions.

Description

technical field [0001] The invention belongs to the field of gene regulation network reasoning in bioinformatics, in particular to a gene network reasoning method based on modular recognition. Background technique [0002] How to accurately elucidate the regulatory relationship between regulatory factors and target genes at the transcriptional level is one of the core challenges of computational biology and bioinformatics in recent years. A more accurate identification of the regulatory relationship between transcriptional regulators and target genes is crucial for exploring the laws of cell growth and division, cell differentiation and development. In addition, the gene regulatory network provides a powerful help for modern medical research, which can simulate and predict disease-causing genes from the perspective of the lowest level of life activities - gene control, which will help medical staff to accurately implement targeted therapy for patients, and have Contribute t...

AI Technical Summary

Problems solved by technology

However, the prerequisite for the application of most classical gene module identification algorithms is the need to know the topology of the gene network, and it is impossible to study gene modules from a data-driven perspective.

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