Deep learning-based ovarian cancer subtypes identification using multi-omics data
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RESEARCH
Open Access
Deep learning-based ovarian cancer subtypes identification using multi-omics data Long-Yi Guo1, Ai-Hua Wu2, Yong-xia Wang2, Li-ping Zhang1, Hua Chai3* and Xue-Fang Liang2* * Correspondence: chaih3@mail. sysu.edu.cn; liangxuefang2006@126. com 3 School of Data and Computer Science, Sun Yat-sen University, Guangzhou 510000, China 2 Center for Reproductive Medicine, Guangdong Hospital of Traditional Chinese Medicine, Guangzhou 510120, China Full list of author information is available at the end of the article
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. Keywords: Ovarian cancer, Deep learning, Multi-omics
Background Ovarian cancer is one of the most common gynecologic cancers in the world that rank third after cervical and uterine cancer, and its mortality rate is high. Therefore, it is very important to know more about the ovarian cancer heterogeneity for choosing different treatment responses and predicting patients’ clinical outcomes. One way to research the heterogeneity is identifying different molecular subtypes in ovarian cancer, and many machine learning methods have been proposed for solving this problem [1, 2]. With the development of biological sequencing technology, different kinds of © The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons
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