Version 1
: Received: 16 May 2020 / Approved: 17 May 2020 / Online: 17 May 2020 (08:50:22 CEST)
How to cite:
Bhattacharjee, A.; Vishwakarma, G.K.; Banerjee, S.; Shukla, S. How to Analyze Cancer Progression in COVID-19 Pandemic?. Preprints.org2020, 2020050288. https://doi.org/10.20944/preprints202005.0288.v1.
Bhattacharjee, A.; Vishwakarma, G.K.; Banerjee, S.; Shukla, S. How to Analyze Cancer Progression in COVID-19 Pandemic?. Preprints.org 2020, 2020050288. https://doi.org/10.20944/preprints202005.0288.v1.
Cite as:
Bhattacharjee, A.; Vishwakarma, G.K.; Banerjee, S.; Shukla, S. How to Analyze Cancer Progression in COVID-19 Pandemic?. Preprints.org2020, 2020050288. https://doi.org/10.20944/preprints202005.0288.v1.
Bhattacharjee, A.; Vishwakarma, G.K.; Banerjee, S.; Shukla, S. How to Analyze Cancer Progression in COVID-19 Pandemic?. Preprints.org 2020, 2020050288. https://doi.org/10.20944/preprints202005.0288.v1.
Abstract
The constant news about the corona virus is scary. It is not possible to separate treatment for Cancer due to COVID-19. An effective treatment comparison strategy is needed. We need to have a handy tool to understand cancer progression in this unprecedented scenario. Linking different events of cancer progression is the need of the hour. It is a methodological challenge. We provide the solutions to overcome the issue with interval between two consecutive events in motivating head and neck cancer (HNC) data.
Computer Science and Mathematics, Probability and Statistics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.