Comorbidity network analysis and genetics of colorectal cancer

Branimir Ljubic; Martin Pavlovski; Jumanah Alshehri; Shoumik Roychoudhury; Vladimir B. Bajic; Christophe Marc Van Neste; Zoran Obradovic

doi:10.1016/j.imu.2020.100492

Comorbidity network analysis and genetics of colorectal cancer

Branimir Ljubic, Martin Pavlovski, Jumanah Alshehri, Shoumik Roychoudhury, Vladimir B. Bajic, Christophe Marc Van Neste, Zoran Obradovic

Research output: Contribution to journal › Article › peer-review

8 Scopus citations

Abstract

Colorectal 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.

Original language	English (US)
Pages (from-to)	100492
Journal	Informatics in Medicine Unlocked
Volume	21
DOIs	https://doi.org/10.1016/j.imu.2020.100492
State	Published - 2020

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10.1016/j.imu.2020.100492

https://repository.kaust.edu.sa/bitstream/10754/667963/1/1-s2.0-S2352914820306432-main.pdf

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@article{7bec66a0b695441297a699b8932299ff,

title = "Comorbidity network analysis and genetics of colorectal cancer",

abstract = "Colorectal 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.",

author = "Branimir Ljubic and Martin Pavlovski and Jumanah Alshehri and Shoumik Roychoudhury and Bajic, {Vladimir B.} and {Van Neste}, {Christophe Marc} and Zoran Obradovic",

note = "KAUST Repository Item: Exported on 2021-03-09 Acknowledgements: The 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.",

year = "2020",

doi = "10.1016/j.imu.2020.100492",

language = "English (US)",

volume = "21",

pages = "100492",

journal = "Informatics in Medicine Unlocked",

issn = "2352-9148",

publisher = "Elsevier Ltd",

}

TY - JOUR

T1 - Comorbidity network analysis and genetics of colorectal cancer

AU - Ljubic, Branimir

AU - Pavlovski, Martin

AU - Alshehri, Jumanah

AU - Roychoudhury, Shoumik

AU - Bajic, Vladimir B.

AU - Van Neste, Christophe Marc

AU - Obradovic, Zoran

N1 - KAUST Repository Item: Exported on 2021-03-09 Acknowledgements: The 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.

PY - 2020

Y1 - 2020

N2 - Colorectal 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.

AB - Colorectal 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.

UR - http://hdl.handle.net/10754/667963

UR - https://linkinghub.elsevier.com/retrieve/pii/S2352914820306432

U2 - 10.1016/j.imu.2020.100492

DO - 10.1016/j.imu.2020.100492

M3 - Article

SN - 2352-9148

VL - 21

SP - 100492

JO - Informatics in Medicine Unlocked

JF - Informatics in Medicine Unlocked

ER -

Comorbidity network analysis and genetics of colorectal cancer

Abstract

UN SDGs

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