Cancer subtype identification method and system based on self-attention deep learning

Abstract

The invention provides a cancer subtype identification method and system based on self-attention deep learning, and the method comprises the following steps: firstly, carrying out the preprocessing of multi-omics data of a cancer, then employing a deep learning Dense network to learn the low-dimensional features of each omics, and carrying out the preliminary integration of different omics features through a splicing mode; and then a similarity matrix between samples is constructed by using self-attention, and feature fusion is carried out according to matrix weight and splicing features to obtain final integrated feature representation. And using a decoder to minimize an error between the fusion feature and the original omics feature, and performing adversarial learning of integrated feature distribution through a discriminator. And finally, clustering the learned integrated feature distribution through a Gaussian mixture model to identify cancer subtypes. According to the method, multiple omics data can be effectively integrated, meanwhile, the relation between samples is modeled in a self-adaptive mode, better feature representation is learned, a better clustering result is obtained, and accurate recognition of cancer subtypes is achieved.

Description

technical field [0001] The present invention relates to the field of biological information technology, in particular to a method and system for identifying cancer subtypes based on self-attention deep learning. Background technique [0002] The diagnosis, treatment and prognosis evaluation of cancer is one of the most urgent and important research topics in the field of life science and medicine. Studies have shown that cancer is highly heterogeneous, with the same clinical stage or histological morphology, and its molecular typing is very different, and different molecular typing plays a crucial role in the selection of preoperative treatment options and prognosis of patients It is an important basis for individualized therapy, especially endocrine therapy and targeted therapy. [0003] Early cancer molecular typing research mainly used single omics data. This typing method depends on the type of data used, and the results obtained from different types of omics data are i...

AI Technical Summary

Problems solved by technology

These methods ignore the data characteristics between different omics and the relationship between samples

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