AutoCoV: tracking the early spread of COVID-19 in terms of the spatial and temporal patterns from embedding space by K-mer based deep learning.

Sung, Inyoung; Lee, Sangseon; Pak, Minwoo; Shin, Yunyol; Kim, Sun

Sung, Inyoung; Lee, Sangseon; Pak, Minwoo; Shin, Yunyol; Kim, Sun.

Afiliación

Sung I; Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea.
Lee S; Institute of Computer Technology, Seoul National University, Seoul, Republic of Korea.
Pak M; Department of Computer Science and Engineering, Seoul National University, Seoul, Republic of Korea.
Shin Y; Department of Computer Science and Engineering, Seoul National University, Seoul, Republic of Korea.
Kim S; Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea. sunkim.bioinfo@snu.ac.kr.

BMC Bioinformatics ; 23(Suppl 3): 149, 2022 Apr 25.

Article en En | MEDLINE | ID: mdl-35468739

Asunto(s)

COVID-19; Aprendizaje Profundo; COVID-19/epidemiología; Genoma; Humanos; Pandemias; SARS-CoV-2

Palabras clave

COVID-19; Deep learning; Early spreading pattern; SARS-CoV-2; Sequence embedding

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Aprendizaje Profundo / COVID-19 Límite: Humans Idioma: En Revista: BMC Bioinformatics Asunto de la revista: INFORMATICA MEDICA Año: 2022 Tipo del documento: Article Pais de publicación: Reino Unido

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