ResMiCo: Increasing the quality of metagenome-assembled genomes with deep learning.

Mineeva, Olga; Danciu, Daniel; Schölkopf, Bernhard; Ley, Ruth E; Rätsch, Gunnar; Youngblut, Nicholas D

Mineeva, Olga; Danciu, Daniel; Schölkopf, Bernhard; Ley, Ruth E; Rätsch, Gunnar; Youngblut, Nicholas D.

Afiliación

Mineeva O; Department of Computer Science, ETH Zürich, Zürich, Switzerland.
Danciu D; Department of Empirical Inference, Max Planck Institute for Intelligent Systems, Tübingen, Germany.
Schölkopf B; Swiss Institute for Bioinformatics, Lausanne, Switzerland.
Ley RE; Department of Computer Science, ETH Zürich, Zürich, Switzerland.
Rätsch G; Department of Computer Science, ETH Zürich, Zürich, Switzerland.
Youngblut ND; Department of Empirical Inference, Max Planck Institute for Intelligent Systems, Tübingen, Germany.

PLoS Comput Biol ; 19(5): e1011001, 2023 05.

Article en En | MEDLINE | ID: mdl-37126495

Asunto(s)

Aprendizaje Profundo; Metagenoma; Metagenoma/genética; Genómica/métodos; Análisis de Secuencia de ADN/métodos; Metagenómica; Programas Informáticos

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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Metagenoma / Aprendizaje Profundo Tipo de estudio: Prognostic_studies Idioma: En Revista: PLoS Comput Biol Asunto de la revista: BIOLOGIA / INFORMATICA MEDICA Año: 2023 Tipo del documento: Article País de afiliación: Suiza

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