Developing a label propagation approach for cancer subtype classification problem
Özet
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.
