Table of Content

Application of Deep Learning in Cancer

Submission Deadline: 01 October 2023 Submit to Special Issue

Guest Editors

Prof. Dr. Xiangtao Li, School of Artificial Intelligence, Jilin University, China.

Dr. Yunhe Wang, School of Artificial Intelligence, Hebei University of Technology, China.


With the burst of the large-scale data in bioinformatics and computational biology disciplines, we have witnessed the explosive growth of different studies in cancer field. For instance, drug response prediction in cancer, drug sensitivity prediction of cancer cell lines, cancer prognosis prediction, and cancer-cell clustering. However, traditional studies always suffer from multitudes of challenges, including the dimensionality curse, data noises, data scalability, and data processing. To address these issues, novel computational methods and studies about cancer cells have to be developed. Deep learning has been suggested to be a more generic model, requires less data engineering, and achieves more accurate prediction when dealing with large amounts of data. It has become the hot topic in the field of artificial intelligence. Therefore, we can apply deep learning for the massive amounts of data.


Now we are reaching a new level of interest in the field with the emergence of many new applications and algorithms for deep learning. This Special Issue explores the latest deep learning algorithms and research results in the applications of cancer studies. We also welcome the application of novel algorithms and studies of deep learning in various fields about cancer, such as cancer diagnostics, cancer classification, and others.


We welcome authors to submit original research, review, and perspective articles focusing on, but not limited to, new findings in the following areas:

• Prediction of drug responses or sensitivity in cancer cell lines by deep learning

• Deep learning in cancer prognosis prediction

• Deep learning in high-dimensional data clustering and classification

• Cancer-cell deep learning clustering and classification

• Cancer detection and relevant gene identification using deep learning models

• Basic biological research on cancer by deep learning 


Deep Learning, Machine Learning, Cancer Data, High-Dimensional Data, Cancer Cells Research

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