… been successfully repurposed various viral cancers. Here in this study, a critical review of
viral cancer related databases, tools, and different machine learning, deep learning and virtual …

… deep learning-based method to identify viruses from human RNA sequencing and demonstrate
its ability to rapidly characterize viruses that are expressed in tumors and uncover viral …

… of virus-driven and non-virus driven cancers are sufficiently different to be detectable by artificial
intelligence (AI) through deep learning-… We show that deep transfer learning can predict …

… However, this cancer is often diagnosed in the later stages, which makes treatment difficult
… This study applied deep learning (DL) models for the early prediction of liver cancer in a …

… , deep learning-based detection of EBV in gastric cancer has not been investigated to date.
Clinical adoption of deep learning… any molecular biomarker in gastric cancer. To address this …

… To study the association between the viruses and the cancer status, we built a logistic
regression classifier with Lasso penalty to predict the CRC status based on the bin abundance on …

… leading to cancer. [ 9 ] In summary, ≈15% of human cancer cases are attributed to oncogenic
viruses. [ 10 ] This calls for novel methods and computational tools for better detecting …

… the performance of a deep learning model on independent datasets [33]. Therefore, we used
another viral integration site … She is interested in cancer bioinformatics and deep learning. …

… a robust, accurate and explainable deep learning model, providing novel … virus (EBV) is
one of the first described human cancer viruses and is associated with up to 10 types of cancers, …

… viral reads and assemble viral contigs. We apply viRNAtrap, which is based on a deep
learning model trained to discriminate viral RNAseq reads, to 14 cancer types from The Cancer …