Metastatic State of Colorectal Cancer can be Accurately Predicted with Methylome
Type
Conference PaperKAUST Department
Computer Science ProgramComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
KAUST Grant Number
BAS/1/1606-01-01URF/1/1976
Date
2020-04-17Online Publication Date
2020-04-17Print Publication Date
2019-12-19Permanent link to this record
http://hdl.handle.net/10754/664683
Metadata
Show full item recordAbstract
Colorectal cancer (CRC) appears to be the third most common cancer as well as the fourth most common cause of cancer deaths in the world. Its most lethal states are when it becomes metastatic. It is of interest to find tests that can quickly and accurately determine if the patient has already developed metastasis. Changes in methylation profiles have been found to be characteristic of cancers at different stages and can therefore be used to develop diagnostic panels. We developed a deep learning (DL) model (Deep2Met) using methylation profiles of patients with CRC to predict if the cancer is in its metastatic state. Results suggest that our method achieves an AUPR and an average F-score of 96.99% and 94.71%, respectively, making Deep2Met potentially useful for diagnostic purposes. The DL model Deep2Met we developed, shows promise in the diagnosis of CRC based on methylation profiles of individual patients.Citation
Albaradei, S., Thafar, M., Van Neste, C., Essack, M., & Bajic, V. B. (2019). Metastatic State of Colorectal Cancer can be Accurately Predicted with Methylome. Proceedings of the 2019 6th International Conference on Bioinformatics Research and Applications. doi:10.1145/3383783.3383792Sponsors
This work has been supported by the King Abdullah University of Science and Technology (KAUST) Base Research Fund (BAS/1/1606-01-01) to VBB, and KAUST Office of Sponsored Research (OSR) under Awards No CCF ? URF/1/1976-30-01Publisher
ACMConference/Event name
6th International Conference on Bioinformatics Research and Applications, ICBRA 2019ISBN
9781450372183Additional Links
https://dl.acm.org/doi/10.1145/3383783.3383792ae974a485f413a2113503eed53cd6c53
10.1145/3383783.3383792