Networks of information token recurrences derived from genomic sequences may reveal hidden patterns in epidemic outbreaks: A case study of the 2019-nCoV coronavirus.

Markus Luczak-Roesch

is the (which was not peer-reviewed) The copyright holder for this preprint . https://doi.org/10.1101/2020.02.07.20021139 doi: medRxiv preprint alternative tokenisations are needed, such as weighting the +1, +2 and +3 reading frames differently (or omitting certain frames entirely) depending on their functional role [10] . We also suggest that it will be important to perform a wider variety of analysis of the generated TIC data (e.g. testing of different network clustering methods, application of dynamical systems analyses techniques) to get more profound insights. Our analysis also does not take measurement biases into account that may results from different techniques used by different laboratories for genome extraction and analysis [7] .