Cancer claims millions of lives yearly worldwide. While many therapies have been made
available in recent years, by in large cancer remains unsolved. Exploiting computational …

The screening of compound–protein interactions (CPIs) is one of the most crucial steps in
finding hit and lead compounds. Deep learning (DL) methods for CPI prediction can address …

Drug response prediction in cancer cell lines is of great significance in personalized
medicine. In this study, we propose GADRP, a cancer drug response prediction model …

Drug response prediction is an important problem in personalized cancer therapy. Among
various newly developed models, significant improvement in prediction performance has …

Apprehension of drug action mechanism is paramount for drug response prediction and
precision medicine. The unprecedented development of machine learning and deep …

Accumulated preclinical data are increasingly being re-used to build and validate predictive
models generated by artificial intelligence (AI)[1] algorithms. Such in silico models have a …

The prediction of drug–drug interactions (DDIs) is essential for the development and
repositioning of new drugs. Meanwhile, they play a vital role in the fields of …

The field of omics, driven by advances in high-throughput sequencing, faces a data
explosion. This abundance of data offers unprecedented opportunities for predictive …

Cancer treatment has become one of the biggest challenges in the world today. Different
treatments are used against cancer; drug-based treatments have shown better results. On …

Cancer epigenetics has become increasingly popular due to the reversible nature of
epigenetic changes that tend to take place during carcinogenesis. Cervical tumorigenesis in …