Developing a label propagation approach for cancer subtype classification problem

Güner P., Bakir-Güngör B., Coşkun M.

Turkish Journal of Biology, vol.46, no.2, pp.145-161, 2022 (SCI-Expanded) identifier identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 46 Issue: 2
  • Publication Date: 2022
  • Doi Number: 10.3906/biy-2108-83
  • Journal Name: Turkish Journal of Biology
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, BIOSIS, CAB Abstracts, Veterinary Science Database, TR DİZİN (ULAKBİM)
  • Page Numbers: pp.145-161
  • Keywords: Cancer subtype, bioinformatics, machine learning, label propagation, personalized medicine, NETWORK-BASED STRATIFICATION, EXPRESSION, VALIDATION
  • Kayseri University Affiliated: No


© TÜBİTAK.Cancer is a disease in which abnormal cells grow uncontrollably and invade other tissues. Several types of cancer have various subtypes with different clinical and biological implications. Based on these differences, treatment methods need to be customized. The identification of distinct cancer subtypes is an important problem in bioinformatics, since it can guide future precision medicine applications. In order to design targeted treatments, bioinformatics methods attempt to discover common molecular pathology of different cancer subtypes. Along this line, several computational methods have been proposed to discover cancer subtypes or to stratify cancer into informative subtypes. However, existing works do not consider the sparseness of data (genes having low degrees) and result in an ill-conditioned solution. To address this shortcoming, in this paper, we propose an alternative unsupervised method to stratify cancer patients into subtypes using applied numerical algebra techniques. More specifically, we applied a label propagation-based approach to stratify somatic mutation profiles of colon, head and neck, uterine, bladder, and breast tumors. We evaluated the performance of our method by comparing it to the baseline methods. Extensive experiments demonstrate that our approach highly renders tumor classification tasks by largely outperforming the state-of-the-art unsupervised and supervised approaches.