csORF-finder: an effective ensemble learning framework for accurate identification of multi-species coding short open reading frames.

Zhang, Meng; Zhao, Jian; Li, Chen; Ge, Fang; Wu, Jing; Jiang, Bin; Song, Jiangning; Song, Xiaofeng

Zhang, Meng; Zhao, Jian; Li, Chen; Ge, Fang; Wu, Jing; Jiang, Bin; Song, Jiangning; Song, Xiaofeng.

Afiliación

Zhang M; Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China.
Zhao J; Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China.
Li C; Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia.
Ge F; School of Computer Science and Engineering, Nanjing University of Science and Technology, 200 Xiaolingwei, Nanjing 210094, China.
Wu J; School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing 211166, China.
Jiang B; College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China.
Song J; Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia.
Song X; Monash Data Futures Institute, Monash University, Melbourne, VIC 3800, Australia.

Brief Bioinform ; 23(6)2022 11 19.

Article en En | MEDLINE | ID: mdl-36094083

Asunto(s)

Sistemas de Lectura Abierta; ARN Largo no Codificante; Animales; Ratones; Drosophila melanogaster/genética; Aprendizaje Automático; Péptidos/genética; ARN Largo no Codificante/genética; ARN Mensajero/genética; Humanos

Palabras clave

coding sORFs; deep learning; ensemble learning; feature encoding; in-frame sequence features

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Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Sistemas de Lectura Abierta / ARN Largo no Codificante Tipo de estudio: Diagnostic_studies / Prognostic_studies Límite: Animals / Humans Idioma: En Revista: Brief Bioinform Asunto de la revista: BIOLOGIA / INFORMATICA MEDICA Año: 2022 Tipo del documento: Article País de afiliación: China

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