Cancer prediction using RNA sequencing and deep learning

Abstract

Numerous areas of medical services, including as imaging diagnostics, advanced pathology, emergency clinic confirmation prediction, drug plan, grouping of cancer and stromal cells, specialist help, and so forth, have profited from the utilization of deep learning. Cancer prognosis involves predicting the course of the disease, the likelihood that it will spread and recur, and the likelihood that patients will survive. The clinical management of cancer patients will considerably benefit from the precision of cancer prognosis prediction. To better forecast cancer prognosis, modern statistical analysis and Deep learning techniques are being applied, as well as biomedical translational research being improved. In recent years, the processing capacity has significantly increased and the innovation of artificial insight, especially deep learning, has advanced quickly. Cancer is the leading reason for death in people. As a result, cancer detection is essential for early diagnosis and provides the best chance for treating cancer patients in a secure and efficient manner. It is, however, the trickiest way to increase the likelihood of the person surviving. RNA sequencing has significantly advanced in the last few decades and is now a crucial method for transcriptome profiling.