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

Suyeon Wy1, Daehong Kwon1, Kisang Kwon1

  • 1Department of Biomedical Science and Engineering, Konkuk University, Seoul 05029, Republic of Korea.

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Summary

Biologists can now easily apply deep learning to biological problems using DLEB (Deep Learning Editor for Biologists), a new web platform. DLEB simplifies model design and code generation, overcoming limitations of existing tools for biological research.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Machine Learning in Biology

Background:

  • Deep learning demonstrates significant potential for addressing complex biological problems.
  • Current deep learning platforms often require advanced programming skills and deep learning expertise, posing a barrier for many biologists.
  • Existing tools have limitations that hinder seamless integration into biological research workflows.

Purpose of the Study:

  • To develop a user-friendly web application, DLEB (Deep Learning Editor for Biologists), specifically designed to empower biologists in applying deep learning.
  • To provide biologists with an accessible tool for designing deep learning models and generating executable Python code without extensive programming knowledge.
  • To facilitate the application of deep learning in biological research by simplifying model development and deployment.

Main Methods:

  • Development of a web-based platform, DLEB, featuring an intuitive interface for deep learning model design.
  • Integration of features for automatic Python code generation compatible with direct execution on researchers' machines.
  • Inclusion of functionalities such as model recommendations, biological data pre-processing, and a library of template models and example datasets.

Main Results:

  • DLEB enables biologists to easily design deep learning models tailored for biological applications.
  • The platform generates runnable Python code, reducing the technical expertise required for implementation.
  • DLEB offers valuable supplementary tools, including data pre-processing and model templates, streamlining the deep learning workflow for biologists.

Conclusions:

  • DLEB significantly lowers the barrier for biologists to utilize deep learning in their research.
  • The platform enhances the accessibility and efficiency of applying advanced computational methods to biological challenges.
  • DLEB serves as a crucial resource for advancing biological discovery through the accessible application of deep learning.