Abstract
Abstract
The rapid advancement of single-cell and spatial omics technologies has led to the generation of high-dimensional, large-scale multi-view data that capture complex molecular and cellular interactions. Effectively analyzing these large datasets requires machine learning algorithms capable of uncovering meaningful patterns, predicting disease progression, assessing treatment response.
In this presentation, I will present my work on deep learning-based methods for understanding clinical outcomes in the context of cancer. Specifically, I will focus on my recent analysis of astrocytoma patients using machine learning approaches, highlighting how these methods can reveal patterns of disease progression and prognosis. Additionally, I will briefly present my other studies on the development of machine learning models for the identification of shared molecular and spatial features associated with diseases.