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DeepBIO: an automated and interpretable deep-learning platform for high-throughput biological sequence prediction, functional annotation and visualization analysis.
Wang, Ruheng; Jiang, Yi; Jin, Junru; Yin, Chenglin; Yu, Haoqing; Wang, Fengsheng; Feng, Jiuxin; Su, Ran; Nakai, Kenta; Zou, Quan; Wei, Leyi.
Afiliação
  • Wang R; School of Software, Shandong University, Jinan, China.
  • Jiang Y; Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, China.
  • Jin J; School of Software, Shandong University, Jinan, China.
  • Yin C; Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, China.
  • Yu H; School of Software, Shandong University, Jinan, China.
  • Wang F; Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, China.
  • Feng J; School of Software, Shandong University, Jinan, China.
  • Su R; Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, China.
  • Nakai K; School of Software, Shandong University, Jinan, China.
  • Zou Q; Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, China.
  • Wei L; School of Software, Shandong University, Jinan, China.
Nucleic Acids Res ; 51(7): 3017-3029, 2023 04 24.
Article em En | MEDLINE | ID: mdl-36796796
The development of next-generation sequencing techniques has led to an exponential increase in the amount of biological sequence data accessible. It naturally poses a fundamental challenge­how to build the relationships from such large-scale sequences to their functions. In this work, we present DeepBIO, the first-of-its-kind automated and interpretable deep-learning platform for high-throughput biological sequence functional analysis. It enables researchers to develop new deep-learning architectures to answer any biological question in a fully automated pipeline. We expect DeepBIO to ensure the reproducibility of deep-learning-based biological sequence analysis, lessen the programming and hardware burden for biologists and provide meaningful functional insights at both the sequence level and base level from biological sequences alone.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Aprendizado Profundo Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Aprendizado Profundo Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2023 Tipo de documento: Article