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Bioinformatics and system biology approaches to identify pathophysiological impact of COVID-19 to the progression and severity of neurological diseases.

  • Rahman, Md Habibur1
  • Rana, Humayan Kabir2
  • Peng, Silong3
  • Kibria, Md Golam4
  • Islam, Md Zahidul5
  • Mahmud, S M Hasan6
  • Moni, Mohammad Ali7
  • 1 Dept. of Computer Science and Engineering, Islamic University, Kushtia 7003, Bangladesh. , (Bangladesh)
  • 2 Dept. of Computer Science and Engineering, Green University of Bangladesh, Dhaka, Bangladesh. , (Bangladesh)
  • 3 Institute of Automation, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Beijing 100190, China. , (China)
  • 4 Dept. of Chemical and Petroleum Engineering, Schulich School of Engineering, University of Calgary, Canada. , (Canada)
  • 5 Department of Electronics, Graduate School of Engineering, Nagoya University, Japan. , (Japan)
  • 6 Dept. of Computer Science, American International University Bangladesh, Dhaka, Bangladesh. , (Bangladesh)
  • 7 School of Health and Rehabilitation Sciences, Faculty of Health and Behavioural Sciences, The University of Queensland, St Lucia, QLD 4072, Australia. Electronic address: [email protected] , (Australia)
Published Article
Computers in biology and medicine
Publication Date
Sep 23, 2021
DOI: 10.1016/j.compbiomed.2021.104859
PMID: 34601390


The Coronavirus Disease 2019 (COVID-19) still tends to propagate and increase the occurrence of COVID-19 across the globe. The clinical and epidemiological analyses indicate the link between COVID-19 and Neurological Diseases (NDs) that drive the progression and severity of NDs. Elucidating why some patients with COVID-19 influence the progression of NDs and patients with NDs who are diagnosed with COVID-19 are becoming increasingly sick, although others are not is unclear. In this research, we investigated how COVID-19 and ND interact and the impact of COVID-19 on the severity of NDs by performing transcriptomic analyses of COVID-19 and NDs samples by developing the pipeline of bioinformatics and network-based approaches. The transcriptomic study identified the contributing genes which are then filtered with cell signaling pathway, gene ontology, protein-protein interactions, transcription factor, and microRNA analysis. Identifying hub-proteins using protein-protein interactions leads to the identification of a therapeutic strategy. Additionally, the incorporation of comorbidity interactions score enhances the identification beyond simply detecting novel biological mechanisms involved in the pathophysiology of COVID-19 and its NDs comorbidities. By computing the semantic similarity between COVID-19 and each of the ND, we have found gene-based maximum semantic score between COVID-19 and Parkinson's disease, the minimum semantic score between COVID-19 and Multiple sclerosis. Similarly, we have found gene ontology-based maximum semantic score between COVID-19 and Huntington disease, minimum semantic score between COVID-19 and Epilepsy disease. Finally, we validated our findings using gold-standard databases and literature searches to determine which genes and pathways had previously been associated with COVID-19 and NDs. Copyright © 2021 Elsevier Ltd. All rights reserved.

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