DLEB: a web application for building deep learning models in biological research. Issue Volume 50:Issue W1(2022) (12th May 2022)

• Record Type: Journal Article
• Authors: Wy, Suyeon; Kwon, Daehong; Kwon, Kisang; Kim, Jaebum
• Is Part Of: Nucleic acids research. Volume 50:Issue W1(2022)
• Page Start: W254
• Page End: W260
• Publication Date: 2022-05-12
• Subjects: Nucleic acids -- Periodicals; Molecular biology -- Periodicals; 572.805
• DOI: 10.1093/nar/gkac369
• Languages: English
• ISSNs: 0305-1048

Abstract

Abstract: 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/ . Graphical Abstract: