Publications by authors named "Tsung-Wei Lin"
Multi-task Learning Graph Neural Networks for Cancer Prognosis Prediction with Genomic Data.
Annu Int Conf IEEE Eng Med Biol Soc
July 2024
Providing robust prognosis predictions for cancers with limited data samples remains a challenge for precision oncology. In this study, we propose a novel approach that combines multi-task learning (MTL) and graph neural networks (GNNs) to address this issue. By representing gene-gene interactions as a graph network, our approach leverages multi-task learning to effectively capture the relationships of genes relevant to the oncogenesis and progression of breast, lung, and colon cancer.
View Article and Find Full Text PDFExploiting common patterns in diverse cancer types via multi-task learning.
NPJ Precis Oncol
October 2024
Article Synopsis
- Cancer prognosis needs precision to identify high-risk patients, and our study uses deep learning to simplify complex medical data into useful feature vectors for better predictions across different cancer types.)
- We developed a multi-task bimodal neural network that combines RNA sequencing and clinical data from various cancers, showing significant improvement in prognosis prediction, especially for Colon Adenocarcinoma with substantial increases in relevant metrics.)
- Our approach demonstrates that integrating data from multiple cancer types can enhance predictive accuracy and offers a promising step toward using advanced techniques for personalized medicine in cancer treatment.)