Computational prediction and analysis of species-specific fungi phosphorylation via feature optimization strategy.

Cao, Man; Chen, Guodong; Yu, Jialin; Shi, Shaoping

Cao, Man; Chen, Guodong; Yu, Jialin; Shi, Shaoping.

Afiliación

Cao M; Department of Mathematics and Numerical Simulation and High-Performance Computing Laboratory, School of Sciences, Nanchang University, Nanchang, China.
Chen G; Department of Mathematics and Numerical Simulation and High-Performance Computing Laboratory, School of Sciences, Nanchang University, Nanchang, China.
Yu J; Department of Mathematics and Numerical Simulation and High-Performance Computing Laboratory, School of Sciences, Nanchang University, Nanchang, China.
Shi S; Department of Mathematics and Numerical Simulation and High-Performance Computing Laboratory, School of Sciences, Nanchang University, Nanchang, China.

Brief Bioinform ; 21(2): 595-608, 2020 03 23.

Article en En | MEDLINE | ID: mdl-30590490

Asunto(s)

Biología Computacional/métodos; Hongos/metabolismo; Hongos/clasificación; Fosforilación; Especificidad de la Especie

Palabras clave

fungi; phosphorylation; random forest; two-step feature optimization

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Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Biología Computacional / Hongos Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Brief Bioinform Asunto de la revista: BIOLOGIA / INFORMATICA MEDICA Año: 2020 Tipo del documento: Article País de afiliación: China

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