Comparative analysis and prediction of nucleosome positioning using integrative feature representation and machine learning algorithms.

Han, Guo-Sheng; Li, Qi; Li, Ying

Han, Guo-Sheng; Li, Qi; Li, Ying.

Afiliação

Han GS; Department of Mathematics and Computational Science, Xiangtan University, Xiangtan, 411105, Hunan, China. hangs@xtu.edu.cn.
Li Q; Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education and Hunan Key Laboratory for Computation and Simulation in Science and Engineering, Xiangtan University, Xiangtan, 411105, Hunan, China. hangs@xtu.edu.cn.
Li Y; Department of Mathematics and Computational Science, Xiangtan University, Xiangtan, 411105, Hunan, China.

BMC Bioinformatics ; 22(Suppl 6): 129, 2021 Jun 02.

Article em En | MEDLINE | ID: mdl-34078256

Assuntos

Caenorhabditis elegans; Nucleossomos; Algoritmos; Animais; Caenorhabditis elegans/genética; Drosophila melanogaster/genética; Aprendizado de Máquina; Nucleossomos/genética; Saccharomyces cerevisiae/genética; Máquina de Vetores de Suporte

Palavras-chave

Convolutional neural networks; Extreme gradient boosting; Extreme learning machine; Frequency chaos game representation; Nucleosome classification; Support vector machine

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Nucleossomos / Caenorhabditis elegans Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Animals Idioma: En Revista: BMC Bioinformatics Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2021 Tipo de documento: Article País de afiliação: China

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