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dc.contributor.authorAlbaradei, Somayah
dc.contributor.authorThafar, Maha
dc.contributor.authorVan Neste, Christophe
dc.contributor.authorEssack, Magbubah
dc.contributor.authorBajic, Vladimir B.
dc.date.accessioned2020-08-19T13:43:09Z
dc.date.available2020-08-19T13:43:09Z
dc.date.issued2020-04-17
dc.identifier.citationAlbaradei, 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:10.1145/3383783.3383792
dc.identifier.isbn9781450372183
dc.identifier.doi10.1145/3383783.3383792
dc.identifier.urihttp://hdl.handle.net/10754/664683
dc.description.abstractColorectal 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.
dc.description.sponsorshipThis 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
dc.publisherAssociation for Computing Machinery (ACM)
dc.relation.urlhttps://dl.acm.org/doi/10.1145/3383783.3383792
dc.rightsPermission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distribute for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee
dc.titleMetastatic State of Colorectal Cancer can be Accurately Predicted with Methylome
dc.typeConference Paper
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.conference.date2019-12-19 to 2019-12-21
dc.conference.name6th International Conference on Bioinformatics Research and Applications, ICBRA 2019
dc.conference.locationSeoul, KOR
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionKing Abdulaziz University, Faculty of Computers and Information Systems; Jeddah, Kingdom of Saudi Arabia
dc.contributor.institutionTaif University, Faculty of Computers and Information Technology; Taif, Kingdom of Saudi Arabia
dc.contributor.institutionGhent University, Center for Medical Genetics Ghent (CMGG), Ghent, Belgium
dc.identifier.pages125-130
kaust.personAlbaradei, Somayah
kaust.personThafar, Maha
kaust.grant.numberBAS/1/1606-01-01
kaust.grant.numberURF/1/1976
dc.identifier.eid2-s2.0-85089282187
refterms.dateFOA2020-08-19T13:44:13Z
kaust.acknowledged.supportUnitCCF
kaust.acknowledged.supportUnitKAUST Office of Sponsored Research (OSR)
dc.date.published-online2020-04-17
dc.date.published-print2019-12-19


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