Base-resolution prediction of transcription factor binding signals by a deep learning framework.

Zhang, Qinhu; He, Ying; Wang, Siguo; Chen, Zhanheng; Guo, Zhenhao; Cui, Zhen; Liu, Qi; Huang, De-Shuang

Zhang, Qinhu; He, Ying; Wang, Siguo; Chen, Zhanheng; Guo, Zhenhao; Cui, Zhen; Liu, Qi; Huang, De-Shuang.

Afiliación

Zhang Q; Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, China.
He Y; Institute of Machine Learning and Systems Biology, School of Electronics and Information Engineering, Tongji University, Shanghai, China.
Wang S; Institute of Machine Learning and Systems Biology, School of Electronics and Information Engineering, Tongji University, Shanghai, China.
Chen Z; Institute of Machine Learning and Systems Biology, School of Electronics and Information Engineering, Tongji University, Shanghai, China.
Guo Z; College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China.
Cui Z; Institute of Machine Learning and Systems Biology, School of Electronics and Information Engineering, Tongji University, Shanghai, China.
Liu Q; Institute of Machine Learning and Systems Biology, School of Electronics and Information Engineering, Tongji University, Shanghai, China.
Huang DS; Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, China.

PLoS Comput Biol ; 18(3): e1009941, 2022 03.

Article en En | MEDLINE | ID: mdl-35263332

Asunto(s)

Aprendizaje Profundo; Sitios de Unión; Secuenciación de Inmunoprecipitación de Cromatina; Unión Proteica; Factores de Transcripción/metabolismo

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Aprendizaje Profundo Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: PLoS Comput Biol Asunto de la revista: BIOLOGIA / INFORMATICA MEDICA Año: 2022 Tipo del documento: Article País de afiliación: China Pais de publicación: Estados Unidos

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