TY - GEN
T1 - Metastatic State of Colorectal Cancer can be Accurately Predicted with Methylome
AU - Albaradei, Somayah
AU - Thafar, Maha
AU - Van Neste, Christophe
AU - Essack, Magbubah
AU - Bajic, Vladimir B.
N1 - KAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): BAS/1/1606-01-01, URF/1/1976
Acknowledgements: 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/1/1976-30-01
PY - 2020/5/4
Y1 - 2020/5/4
N2 - 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.
AB - 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.
UR - http://hdl.handle.net/10754/664683
UR - https://dl.acm.org/doi/10.1145/3383783.3383792
UR - http://www.scopus.com/inward/record.url?scp=85089282187&partnerID=8YFLogxK
U2 - 10.1145/3383783.3383792
DO - 10.1145/3383783.3383792
M3 - Conference contribution
SN - 9781450372183
SP - 125
EP - 130
BT - Proceedings of the 2019 6th International Conference on Bioinformatics Research and Applications
PB - ACM
ER -
