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DLEB: a web application for building deep learning models in biological research.
Wy, Suyeon; Kwon, Daehong; Kwon, Kisang; Kim, Jaebum.
Afiliação
  • Wy S; Department of Biomedical Science and Engineering, Konkuk University, Seoul 05029, Republic of Korea.
  • Kwon D; Department of Biomedical Science and Engineering, Konkuk University, Seoul 05029, Republic of Korea.
  • Kwon K; Department of Biomedical Science and Engineering, Konkuk University, Seoul 05029, Republic of Korea.
  • Kim J; Department of Biomedical Science and Engineering, Konkuk University, Seoul 05029, Republic of Korea.
Nucleic Acids Res ; 50(W1): W254-W260, 2022 07 05.
Article em En | MEDLINE | ID: mdl-35552439
Deep learning has been applied for solving many biological problems, and it has shown outstanding performance. Applying deep learning in research requires knowledge of deep learning theories and programming skills, but researchers have developed diverse deep learning platforms to allow users to build deep learning models without programming. Despite these efforts, it is still difficult for biologists to use deep learning because of limitations of the existing platforms. Therefore, a new platform is necessary that can solve these challenges for biologists. To alleviate this situation, we developed a user-friendly and easy-to-use web application called DLEB (Deep Learning Editor for Biologists) that allows for building deep learning models specialized for biologists. DLEB helps researchers (i) design deep learning models easily and (ii) generate corresponding Python code to run directly in their machines. DLEB provides other useful features for biologists, such as recommending deep learning models for specific learning tasks and data, pre-processing of input biological data, and availability of various template models and example biological datasets for model training. DLEB can serve as a highly valuable platform for easily applying deep learning to solve many important biological problems. DLEB is freely available at http://dleb.konkuk.ac.kr/.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Aprendizado Profundo Idioma: En Revista: Nucleic Acids Res Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Aprendizado Profundo Idioma: En Revista: Nucleic Acids Res Ano de publicação: 2022 Tipo de documento: Article