Cargando…

DLEB: a web application for building deep learning models in biological research

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 buil...

Descripción completa

Detalles Bibliográficos
Autores principales: Wy, Suyeon, Kwon, Daehong, Kwon, Kisang, Kim, Jaebum
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9252827/
https://www.ncbi.nlm.nih.gov/pubmed/35552439
http://dx.doi.org/10.1093/nar/gkac369
_version_ 1784740358602620928
author Wy, Suyeon
Kwon, Daehong
Kwon, Kisang
Kim, Jaebum
author_facet Wy, Suyeon
Kwon, Daehong
Kwon, Kisang
Kim, Jaebum
author_sort Wy, Suyeon
collection PubMed
description 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/.
format Online
Article
Text
id pubmed-9252827
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Oxford University Press
record_format MEDLINE/PubMed
spelling pubmed-92528272022-07-05 DLEB: a web application for building deep learning models in biological research Wy, Suyeon Kwon, Daehong Kwon, Kisang Kim, Jaebum Nucleic Acids Res Web Server Issue 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/. Oxford University Press 2022-05-12 /pmc/articles/PMC9252827/ /pubmed/35552439 http://dx.doi.org/10.1093/nar/gkac369 Text en © The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Web Server Issue
Wy, Suyeon
Kwon, Daehong
Kwon, Kisang
Kim, Jaebum
DLEB: a web application for building deep learning models in biological research
title DLEB: a web application for building deep learning models in biological research
title_full DLEB: a web application for building deep learning models in biological research
title_fullStr DLEB: a web application for building deep learning models in biological research
title_full_unstemmed DLEB: a web application for building deep learning models in biological research
title_short DLEB: a web application for building deep learning models in biological research
title_sort dleb: a web application for building deep learning models in biological research
topic Web Server Issue
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9252827/
https://www.ncbi.nlm.nih.gov/pubmed/35552439
http://dx.doi.org/10.1093/nar/gkac369
work_keys_str_mv AT wysuyeon dlebawebapplicationforbuildingdeeplearningmodelsinbiologicalresearch
AT kwondaehong dlebawebapplicationforbuildingdeeplearningmodelsinbiologicalresearch
AT kwonkisang dlebawebapplicationforbuildingdeeplearningmodelsinbiologicalresearch
AT kimjaebum dlebawebapplicationforbuildingdeeplearningmodelsinbiologicalresearch