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Deep learning-based multi-functional therapeutic peptides prediction with a multi-label focal dice loss function.
Fan, Henghui; Yan, Wenhui; Wang, Lihua; Liu, Jie; Bin, Yannan; Xia, Junfeng.
Afiliação
  • Fan H; Information Materials and Intelligent Sensing Laboratory of Anhui Province and Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Institutes of Physical Science and Information Technology, Anhui University, Hefei, Anhui 230601, China.
  • Yan W; Information Materials and Intelligent Sensing Laboratory of Anhui Province and Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Institutes of Physical Science and Information Technology, Anhui University, Hefei, Anhui 230601, China.
  • Wang L; Information Materials and Intelligent Sensing Laboratory of Anhui Province and Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Institutes of Physical Science and Information Technology, Anhui University, Hefei, Anhui 230601, China.
  • Liu J; Information Materials and Intelligent Sensing Laboratory of Anhui Province and Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Institutes of Physical Science and Information Technology, Anhui University, Hefei, Anhui 230601, China.
  • Bin Y; Information Materials and Intelligent Sensing Laboratory of Anhui Province and Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Institutes of Physical Science and Information Technology, Anhui University, Hefei, Anhui 230601, China.
  • Xia J; Information Materials and Intelligent Sensing Laboratory of Anhui Province and Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, Institutes of Physical Science and Information Technology, Anhui University, Hefei, Anhui 230601, China.
Bioinformatics ; 39(6)2023 06 01.
Article em En | MEDLINE | ID: mdl-37216900
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Aprendizado Profundo Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2023 Tipo de documento: Article
Texto completo
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XML
PubMed Links
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Aprendizado Profundo Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2023 Tipo de documento: Article