MSc Thesis Defense by: Achini Herath

The diversity and complexity of cancer pose significant challenges in creating target treatment strategies. Identifying molecular subtypes of cancer is crucial for recognizing patients with distinct molecular profiles, thereby enhancing the accuracy of diagnosis, prognosis, and treatment decisions. With recent advancements in technology, there's been a significant increase in the availability of multi-omics data, which is instrumental in the understanding of different cancer subtypes. However, accurately subtyping cancer is difficult due to the high dimensionality and heterogeneity of omics data. Current research in subtype identification often consolidates multi-omics data into a single dataset through simple concatenation and then employs machine learning models to derive a lower-dimensional representation, neglecting the unique distributions of different omics data types. Additionally, they separate representation learning and clustering into two stages, initially learning latent representations and then applying clustering algorithms, leading to suboptimal results due to overlooking the intrinsic clustering structures in the initial learning phase. To address these limitations, we propose a novel deep unsupervised learning model, Subtype-MMCC that combines a multi-modal architecture with decoupled contrastive clustering to create an end-to-end framework. Tested on eight TCGA cancer datasets, Subtype-MMCC outperforms existing clustering methods, with its efficacy further validated by survival and clinical analysis outcomes.

Keywords: Cancer Subtyping; Multi-omics Data; Multi-modal; Contrastive Clustering

 

Internal Reader: Dr. Alioune Ngom

External Reader: Dr. Esam Abdel-Raheem           

Advisor: Dr. Ziad Kobti

Chair: Dr. Muhammad Asaduzzaman