Author: Alejandro Lopez-Rincon; Alberto Tonda; Lucero Mendoza-Maldonado; Eric Claassen; Johan Garssen; Aletta D. Kraneveld
Title: Accurate Identification of SARS-CoV-2 from Viral Genome Sequences using Deep Learning Document date: 2020_3_14
ID: c2lljdi7_11
Snippet: Differently from previous works in literature, that use of deep learning with 65 fixed length features and one-hot label encoding, in this work we propose the use of a different encoding to input the full sequence as a whole. In addition, we use as base input 31,029 as an input vector, which is the maximum length of available to distinguish from other Coronaviruses, due to their genetic similarity. In addition, people with SARS-CoV-2 may present .....
Document: Differently from previous works in literature, that use of deep learning with 65 fixed length features and one-hot label encoding, in this work we propose the use of a different encoding to input the full sequence as a whole. In addition, we use as base input 31,029 as an input vector, which is the maximum length of available to distinguish from other Coronaviruses, due to their genetic similarity. In addition, people with SARS-CoV-2 may present other infections besides the virus [9, 10] . Therefore, it is important to be able to properly classify SARS-CoV-2 from other Coronaviruses.
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