MultiGATAE: A Novel Cancer Subtype Identification Method Based on Multi-Omics and Attention Mechanism

Abstract

Cancer is one of the leading causes of death worldwide, which brings an urgent need for its effective treatment. However, cancer is highly heterogeneous, meaning that one cancer can be divided into several subtypes with distinct pathogenesis and outcomes. This is considered as the main problem which limits the precision treatment of cancer. Thus, cancer subtypes identification is of great importance for cancer diagnosis and treatment. In this work, we propose a deep learning method which is based on multi-omics and attention mechanism to effectively identify cancer subtypes. We first used similarity network fusion to integrate multi-omics data to construct a similarity graph. Then, the similarity graph and the feature matrix of the patient are input into a graph autoencoder composed of a graph attention network and omics-level attention mechanism to learn embedding representation. The K-means clustering method is applied to the embedding representation to identify cancer subtypes. The experiment on eight TCGA datasets confirmed that our proposed method performs better for cancer subtypes identification when compared with the other state-of-the-art methods. The source codes of our method are available at https://github.com/kataomoi7/multiGATAE.

MSC-CSMC: A multi-objective semi-supervised clustering algorithm based on constraints selection and multi-source constraints for gene expression data

“…Generally, there are two approaches to determine the value of k; one is to directly set it as the true number of clusters (Yu et al, 2018;Zhao et al, 2021;Liu et al, 2022;Wu and Ma, 2022); The other approach is applicable to the case where the true number of clusters is unknown, in which the variation range of k is determined firstly, and the k corresponding to the optimal value of an index (Silhouette index, Dunn index, Davies-Bouldin index, etc.) can be chosen as the optimal number of clusters (Gao et al, 2019;Acharya et al, 2020;López-Cortés et al, 2020;Zhang et al, 2022). In this paper, we adopt the first approach, and the number of clusters k is selected according to Table 1.…”

Attention-based GCN Integrates Multi-omics Data for Breast Cancer Subtype Classification and Patient-specific Gene Marker Identification

“…Many multi-omics integrated analysis approaches are based on machine learning techniques, aiming at patient stratification [12][13][14], biomarker discovery [15,16], pathway analysis [17], and drug discovery [18]. One of the challenges of multi-omics analysis is the high dimensionality of data and many studies have proposed different deep learning integration strategies [19][20][21].…”