TargetMM: Accurate Missense Mutation Prediction by Utilizing Local and Global Sequence Information with Classifier Ensemble.

Ge, Fang; Hu, Jun; Zhu, Yi-Heng; Arif, Muhammad; Yu, Dong-Jun

Ge, Fang; Hu, Jun; Zhu, Yi-Heng; Arif, Muhammad; Yu, Dong-Jun.

Afiliación

Ge F; School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094,China.
Hu J; College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023,China.
Zhu YH; School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094,China.
Arif M; School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094,China.
Yu DJ; School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094,China.

Comb Chem High Throughput Screen ; 25(1): 38-52, 2022.

Article en En | MEDLINE | ID: mdl-33280588

Asunto(s)

Algoritmos; Mutación Missense; Humanos; Proteínas/química; Programas Informáticos

Palabras clave

Human disease; classifier ensemble; feature extracting; missense mutation; mutation prediction; proteins.

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Algoritmos / Mutación Missense Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Comb Chem High Throughput Screen Asunto de la revista: BIOLOGIA MOLECULAR / QUIMICA Año: 2022 Tipo del documento: Article País de afiliación: China

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