Author: Gurjit S. Randhawa; Maximillian P.M. Soltysiak; Hadi El Roz; Camila P.E. de Souza; Kathleen A. Hill; Lila Kari
Title: Machine learning using intrinsic genomic signatures for rapid classification of novel pathogens: COVID-19 case study Document date: 2020_2_4
ID: cetdqgff_7
Snippet: • A comprehensive and "in minutes" analysis of a dataset of 5538 unique viral 100 genomic sequences, for a total of 61.8 million bp analyzed, with high classification 101 accuracy scores at all levels, from the highest to the lowest taxonomic rank; ignored to address possible issues arising from sequence length bias......
Document: • A comprehensive and "in minutes" analysis of a dataset of 5538 unique viral 100 genomic sequences, for a total of 61.8 million bp analyzed, with high classification 101 accuracy scores at all levels, from the highest to the lowest taxonomic rank; ignored to address possible issues arising from sequence length bias.
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