Bajic, Vladimir B.
Van Neste, Christophe Marc
KAUST DepartmentApplied Mathematics and Computational Science Program
Computational Bioscience Research Center (CBRC)
Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
Permanent link to this recordhttp://hdl.handle.net/10754/667963
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AbstractColorectal cancer (CRC) is the third most common cancer in the United States and the second leading cause of cancer death. The goal was to identify comorbidities and genes associated with CRC. Methods A novel social network model was developed on the Healthcare Cost and Utilization Project (HCUP) - State Inpatient Databases (SID) California database to study comorbidities of CRC. Ranked lists of comorbidities and comorbidity networks were created, and the prevalence of comorbidities in different stages of CRC was calculated. Ranked lists of comorbidities were utilized for text mining of PubMed and DisGeNET to extract genes associated with CRC.
CitationLjubic, B., Pavlovski, M., Alshehri, J., Roychoudhury, S., Bajic, V., Van Neste, C., & Obradovic, Z. (2020). Comorbidity network analysis and genetics of colorectal cancer. Informatics in Medicine Unlocked, 21, 100492. doi:10.1016/j.imu.2020.100492
SponsorsThe authors gratefully acknowledge the support of the King Abdullah University of Science and Technology's Center Partnership Fund Program. Healthcare Cost and Utilization Project (HCUP), Agency for Healthcare Research and Quality provided data used in this study. The authors also thank Stefan Obradovic for proofreading and editing the language of the manuscript.
JournalInformatics in Medicine Unlocked
Except where otherwise noted, this item's license is described as This is an open access article under the CC BY-NC-ND license.