MSc Thesis Proposal "Multimodal Contrastive Clustering: Deep Unsupervised Learning Approach for Cancer Subtype Discovery with Multi-Omics Data" By: Achini Herath

Multimodal Contrastive Clustering: Deep Unsupervised Learning approach for Cancer Subtype Discovery with Multi-omics data
MSc Thesis Proposal by:
Achini Herath

Date: Wednesday, January 31st, 2024
Time: 11.30 a.m. – 1.00 p.m.
Location: Essex Hall Room 105

Abstract: The diverse and complex nature of cancer presents significant obstacles in developing targeted treatment approaches. The identification of cancer subtypes aims to detect patients with distinct molecular profiles and thus could provide effective diagnosis, prognosis, and treatment of cancer. 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 typically combines multi-omics data into a single dataset through simple concatenation and then employs deep learning models to derive a lower-dimensional representation, neglecting the unique distributions of different omics data types. Additionally, they separate representation learning and sample clustering into two stages, initially learning latent representations and then applying traditional clustering algorithms, which leads to suboptimal results due to overlooking the intrinsic clustering structures in the initial learning phase. To address these limitations, we propose a deep unsupervised learning model, Subtype-MMCC that combined multi-modal learning 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: Clustering

Thesis Committee:
Internal Reader: Dr. Alioune Ngom
External Reader: Dr. Esam Abdel-Raheem
Advisor: Dr. Ziad Kobti