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Paper   IPM / Biological Sciences / 16598
School of Biological Sciences
  Title:   Classifying Breast Cancer Molecular Subtypes using Deep Clustering Approach
  Author(s): 
1.  Narjes Rohani
2.  Changiz Eslahchi
  Status:   Preprint
  Journal: Frontiers in Genetics
  Year:  2021
  Pages:   https://doi.org/10.21203/rs.2.19530/v1
  Supported by:  IPM
  Abstract:
Cancer is a complex disease with a high rate of mortality. The characteristics of tumor masses are very heterogeneous; thus, the appropriate classification of tumors is a critical point in the correct treatment. A high level of heterogeneity has also been observed in breast cancer. Therefore, detecting the molecular subtypes of this disease is a worthwhile issue for medicine that could be facilitated using bioinformatics. Method: Numerous methods have already classified breast cancer based on gene expression data; however, they are not reliable due to the dynamic nature of these data. In contrast, gene mutation data are relatively stable and may lead to better classification. The aim of this study is to introduce a novel method for detecting the molecular subtypes of breast cancer. In this study, somatic mutation profiles of tumors are used; nonetheless, the somatic mutation profiles are very sparse. To address this issue, we made use of the network propagation method on gene interaction network and made the mutation profiles dense. Afterward, we used deep embedded clustering (DEC) method to classify breast tumors into four subtypes. In the next step, gene signatures of each subtype obtained by Fisher exact test and Benjamini-Hochberg procedure.


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