MOTIVATION: Survival analysis is an important tool for modeling time-to-event data, e.g. to predict the survival time of patient after a cancer diagnosis or a certain treatment. While deep neural networks work well in standard prediction tasks, it is...
Determining cancer subtypes and estimating patient prognosis are crucial for cancer research. The massive amount of multi-omics data generated by high-throughput sequencing technology is an important resource for cancer prognosis. Deep learning metho...
MOTIVATION: The process of drug development is inherently complex, marked by extended intervals from the inception of a pharmaceutical agent to its eventual launch in the market. Additionally, each phase in this process is associated with a significa...
MOTIVATION: The size of available omics datasets is steadily increasing with technological advancement in recent years. While this increase in sample size can be used to improve the performance of relevant prediction tasks in healthcare, models that ...
Differentiating cancer subtypes is crucial to guide personalized treatment and improve the prognosis for patients. Integrating multi-omics data can offer a comprehensive landscape of cancer biological process and provide promising ways for cancer dia...