Developing a label propagation approach for cancer subtype identification problem

Turkish Journal of Biology

article

GÜNER, PINAR¹  GÜNGÖR, BURCU¹  COŞKUN, MUSTAFA¹ 

[1] Department of Computer Engineering, Faculty of Engineering, Abdullah Gül University

关键词: Cancer subtype;    bioinformatics;    machine learning;    label propagation;    personalized medicine;   

DOI  :  10.55730/1300-0152.2582

学科分类：生物科学（综合）

来源: Scientific and Technical Research Council of Turkey - TUBITAK

【 摘 要 】

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 propagationbased 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.

【 授权许可】

Unknown