An application of slow feature analysis to the genetic sequences of coronaviruses and influenza viruses

Abstract Background Mathematical approaches have been for decades used to probe the structure of nucleotide sequences. This has led development Bioinformatics. In this exploratory work, a novel mathematical method is applied genetic two related viral families: those coronaviruses and influenza viruses. The are SARS-CoV-2, SARS-CoV-1, MERS. viruses include H1N1-1918, H1N1-2009, H2N2-1957, H3N2-1968. Methods slow feature analysis (SFA), rather new but promising delineate complex in Results indicates that sequences exhibit an elaborate convoluted akin networks. We define measure complexity show each sequence exhibits certain degree within itself, while at same time there exists inter-relationships between family families. From these relationships, we find evidence, especially coronavirus family, increasing associated with higher transmission rate lower mortality. Conclusions defined here may hold promise could become useful tool prediction mortality rates future strains.