文献类型：期刊文献

英文题名：Subtype-Former: a deep learning approach for cancer subtype discovery with multi-omics data

作者：Yang, Hai[1]; Sheng, Yuhang[1]; Jiang, Yi[2]; Fang, Xiaoyang[3]; Li, Dongdong[1]; Zhang, Jing[1]; Wang, Zhe[1]

机构：[1] Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai, 200237, China; [2] Department of Epidemiology and Biostatistics, Tongji Medical College, Huazhong University of Science and Technology, Hubei, 430030, China; [3] Jacobs Technion-Cornell Institute, Cornell Tech, Cornell University, New York, 10044, United States

年份：2022

外文期刊名：arXiv

收录：EI(收录号：20220314920)

语种：英文

外文关键词：Diseases - Formal languages - HTTP - K-means clustering - Learning systems

摘要：Motivation: Cancer is heterogeneous, affecting the precise approach to personalized treatment. Accurate subtyping can lead to better survival rates for cancer patients. High-throughput technologies provide multiple omics data for cancer subtyping. However, precise cancer subtyping remains challenging due to the large amount and high dimensionality of omics data. Results: This study proposed Subtype-Former, a deep learning method based on MLP and Transformer Block, to extract the low-dimensional representation of the multi-omics data. K-means and Consensus Clustering are also used to achieve accurate subtyping results. We compared Subtype- Former with the other state-of-the-art subtyping methods across the TCGA 10 cancer types. We found that Subtype-Former can perform better on the benchmark datasets of more than 5000 tumors based on the survival analysis. In addition, Subtype-Former also achieved outstanding results in pan-cancer subtyping, which can help analyze the commonalities and differences across various cancer types at the molecular level. Finally, we applied Subtype-Former to the TCGA 10 types of cancers. We identified 50 essential biomarkers, which can be used to study targeted cancer drugs and promote the development of cancer treatments in the era of precision medicine. Availability: All the data is available in The Cancer Genome Atlas (https://www.cancer.gov/tcga). The source codes of Subtype-Former are available at https://github.com/haiyangLab/Subtype-Former. ? 2022, CC BY.