Deep Learning for Predicting Gene Regulatory Networks: A Step-by-Step Protocol in R

Area of Science:

• Computational Biology
• Bioinformatics
• Systems Biology

Background:

• Deep learning excels at complex biological problems like gene regulatory network reconstruction.
• Current deep learning tools require programming skills, limiting biologist accessibility.
• Gene regulatory networks involve transcription factors and their target genes.

Purpose of the Study:

• To present an accessible deep learning protocol for biologists using R/RStudio.
• To enable genome-wide prediction of transcription factor-gene regulatory interactions.
• To lower the barrier for applying advanced computational methods in biological research.

Main Methods:

• Utilized TensorFlow and Keras API within R/RStudio.
• Developed a protocol for data preprocessing, neural network design, and training.
• Employed publicly available gene expression data and benchmarks for validation.

Main Results:

• Successfully predicted genome-wide regulatory interactions between transcription factors and genes.
• Provided insights into deep learning model parameter tuning.
• Demonstrated the protocol's effectiveness for novel regulatory association forecasting.

Conclusions:

• The protocol makes deep learning accessible for predicting gene regulatory networks.
• Researchers can gain practical experience applying deep learning to biological data.
• The protocol is adaptable for various research questions in computational biology.