Deep learning-based ovarian cancer subtypes identification using multi-omics data

Abstract

Background: Identifying molecular subtypes of ovarian cancer is important. Compared to identify subtypes using single omics data, the multi-omics data analysis can utilize more information. Autoencoder has been widely used to construct lower dimensional representation for multi-omics feature integration. However, learning in the deep architectures in Autoencoder is difficult for achieving satisfied generalization performance. To solve this problem, we proposed a novel deep learning-based framework to robustly identify ovarian cancer subtypes by using denoising Autoencoder. Results: In proposed method, the composite features of multi-omics data in the Cancer Genome Atlas were produced by denoising Autoencoder, and then the generated lowdimensional features were input into k-means for clustering. At last based on the clustering results, we built the light-weighted classification model with L1-penalized logistic regression method. Furthermore, we applied the differential expression analysis and WGCNA analysis to select target genes related to molecular subtypes. We identified 34 biomarkers and 19 KEGG pathways associated with ovarian cancer. Conclusions: The independent test results in three GEO datasets proved the robustness of our model. The literature reviewing show 19 (56%) biomarkers and 8(42.1%) KEGG pathways identified based on the classification subtypes have been proved to be associated with ovarian cancer. The outcomes indicate that our proposed method is feasible and can provide reliable results.

Multimodal deep learning for biomedical data fusion: a review

“…For the same clinical task, a similar workflow has been adapted by researchers, but applying denoising AEs (DAEs) [ 91 ] instead [ 35 , 36 ]. By adding noise to the input \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$x$\end{document} , but not to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$x$\end{document} in the reconstruction loss (Equation 1 ), the DAE has to learn a reconstruction and also remove noise to approximate the uncorrupted vector x .…”

“…The applications of early nonlinear fusion methods reviewed here have shown that these methods can outperform shallow methods on prediction tasks (e.g. [ 35 , 36 ]). This demonstrates that DL methods are viable alternatives to traditional methods even when sample sizes are comparatively low, because there were as low as 96 patients in the applications reviewed above [ 28 ].…”

Applied machine learning in cancer research: A systematic review for patient diagnosis, classification and prognosis

“…Most of the studies utilized DL architectures for the analysis of imaging and genomic data with respect to risk prediction and stratification. Indicatively, in [64] , [65] , [66] , [67] , [68] , [69] DL models were trained to classify and detect disease subtypes based on images and genetic data. These data-driven approaches demonstrated the superiority of ML-based frameworks towards exploiting heterogeneous datasets with respect to improved diagnosis and treatment.…”

The Role of Omics Approaches to Characterize Molecular Mechanisms of Rare Ovarian Cancers: Recent Advances and Future Perspectives

“…Another study by Elias and colleagues carried out neural network analysis on ncRNA data from EOCs to produce an algorithm to diagnose EOC [92]. A deep learning-based approach has also been used to analyze multi-omics data from ovarian cancers and identify subtypes [93].…”