by Shudeb Babu Sen Omit 1,* 
, Md Mohiuddin 2 , Salma Akhter 3 , Md. Hasan Imam 1 , A. K. M. Mostofa Kamal Habib 4 , Syed Mohammad Meraz Hossain 5 and Nitun Kumar Podder 6
1 Institute of Information Technology, Noakhali Science and Technology University, Noakhali, 3814, Bangladesh
2 Department of Mechanical Engineering, Chittagong University of Engineering and Technology, Chattogram, 4349, Bangladesh
3 Department of Environmental Science and Disaster Management, Noakhali Science and Technology University, Noakhali 3814, Bangladesh
4 National Skills Development Authority, Dhaka, Bangladesh
5 Department of Information and Communication Technology, Dhaka, Bangladesh
6 Department of Computer Science and Engineering, Pabna University of Science and Technology, Pabna, 6600, Bangladesh
* Author to whom correspondence should be addressed.
Journal of Engineering Research and Sciences, Volume 3, Issue 4, Page # 32-41, 2024; DOI: 10.55708/js0304004
Keywords: COVID-19, Comorbidity Identification, Transcriptomic Data, Tippett’s Method, Euclidean Distance
Received: 25 January 2024, Revised: 02 April 2024, Accepted: 05 April 2024, Published Online: 30 April 2024
APA Style
Omit, S. B. S., Mohiuddin, M., Akhter, S., Imam, M. H., Habib, A. K. M. M. K., Hossain, S. M. M., & Podder, N. K. (2024). Computational and bioinformatics approaches for identifying comorbidities of COVID-19 using transcriptomic data. Journal of Engineering Research and Sciences, 3(4), 32-41. https://doi.org/10.55708/js0304004
Chicago/Turabian Style
Omit, Shudeb Babu Sen, Md Mohiuddin, Salma Akhter, Md. Hasan Imam, A. K. M. Mostofa Kamal Habib, Syed Mohammad Meraz Hossain, and Nitun Kumar Podder. “Computational and Bioinformatics Approaches for Identifying Comorbidities of COVID-19 Using Transcriptomic Data.” Journal of Engineering Research and Sciences 3, no. 4 (2024): 32-41. doi:10.55708/js0304004.
IEEE Style
Comorbidity is the co-existence of one or more diseases that occur concurrently or after the primary disease. Patients may have developed comorbidities for COVID-19 that cause harm to the patient’s organs. Besides, patients with existing comorbidities are at high risk, since mortality rates are strongly influenced by comorbidities or former health conditions. Therefore, we developed a computational and bioinformatics model to identify the comorbidities of COVID-19 utilizing transcriptome datasets of patient’s whole blood cells. In our model, we employed gene expression analysis to identify dysregulated genes and curated diseases from Gold Benchmark databases using the dysregulated genes. Subsequently, Tippett’s Method is used for COVID-19’s P-value calculation, and according to the P-value, Euclidean distances are calculated between COVID-19 and the collected diseases. Then the collected diseases are ordered and clustered based on the Euclidean distance. Finally, comorbidities are selected from the top clusters based on a comprehensive literature search. Applying the model, we found that acute myelocytic leukemia, cancer of urinary tract, body weight changes, abdominal aortic aneurysm, kidney neoplasm, diabetes mellitus, and some other rare diseases have correlation with COVID-19 and many of them reveal as comorbidity. Since comorbidities are in conjunction with the primary disease, thus similar drugs and treatments can be used to recover both COVID-19 and its comorbidities by further research. We also proposed that this model can be further useful for detecting comorbidities of other diseases as well.
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