Precise Prediction of Promoter Strength Based on a De Novo Synthetic Promoter Library Coupled with Machine Learning.

Zhao, Mei; Yuan, Zhenqi; Wu, Longtao; Zhou, Shenghu; Deng, Yu

Zhao, Mei; Yuan, Zhenqi; Wu, Longtao; Zhou, Shenghu; Deng, Yu.

Afiliação

Zhao M; National Engineering Laboratory for Cereal Fermentation Technology (NELCF), Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China.
Yuan Z; Jiangsu Provincial Research Center for Bioactive Product Processing Technology, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China.
Wu L; School of Food and Biological Engineering, Jiangsu University, 301 Xuefu Road, Zhenjiang, Jiangsu 212013, China.
Zhou S; School of Artificial Intelligence and Computer Science, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China.
Deng Y; College of Physics and Optoelectronics, Taiyuan University of Technology, Taiyuan 030024, China.

ACS Synth Biol ; 11(1): 92-102, 2022 01 21.

Article em En | MEDLINE | ID: mdl-34927418

Assuntos

Inteligência Artificial; Redes e Vias Metabólicas; Biblioteca Gênica; Aprendizado de Máquina; Regiões Promotoras Genéticas/genética

Palavras-chave

Pearson correlation coefficient; XgBoost model; evolution; machine learning; promoter

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Inteligência Artificial / Redes e Vias Metabólicas Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: ACS Synth Biol Ano de publicação: 2022 Tipo de documento: Article País de afiliação: China

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