Clinical oncology is experiencing rapid growth in data that are collected to enhance cancer
care. With recent advances in the field of artificial intelligence (AI), there is now a …

Research into the potential benefits of artificial intelligence for comprehending the intricate
biology of cancer has grown as a result of the widespread use of deep learning and …

The influx of deep learning (DL) techniques into the field of survival analysis in recent years
has led to substantial methodological progress; for instance, learning from unstructured or …

With the rapid advances in high-throughput sequencing technologies, the focus of survival
analysis has shifted from examining clinical indicators to incorporating genomic profiles with …

Background Transfer learning is a form of machine learning where a pre-trained model
trained on a specific task is reused as a starting point and tailored to another task in a …

Abstract Machine learning (ML) is widely used to uncover structure–property relationships of
materials due to its ability to quickly find potential data patterns and make accurate …

Abstract Domain shift, the mismatch between training and testing data characteristics,
causes significant degradation in the predictive performance in multi-source imaging …

Most machine learning-based methods predict outcomes rather than understanding
causality. Machine learning methods have been proved to be efficient in finding correlations …

The exponential growth of big data in RNA biology (RB) has led to the development of deep
learning (DL) models that have driven crucial discoveries. As constantly evidenced by DL …

Predicting drug synergy is critical to tailoring feasible drug combination treatment regimens
for cancer patients. However, most of the existing computational methods only focus on data …