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Neural Regulation

Neural Regulation

Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.

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Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

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Gene regulatory network prediction using machine learning, deep learning, and hybrid approaches.

Sai Teja Mummadi¹, Md Khairul Islam², Victor Busov³

¹Department of Computer Science, Michigan Technological University, Houghton, MI, USA.

Forestry Research

|September 19, 2025

Summary

Hybrid and transfer learning models accurately predict gene regulatory networks (GRNs) in plants. These advanced methods improve understanding of plant gene regulation, even with limited data in non-model species.

Keywords:

Convolutional neural network Deep learning Gene regulatory network Machine learning Transfer learning

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Area of Science:

Plant biology
Computational biology
Genomics

Background:

Gene regulatory networks (GRNs) are crucial for understanding biological processes.
Elucidating GRNs aids in deciphering metabolic pathways and complex traits.

Purpose of the Study:

To develop and evaluate machine learning, deep learning, and hybrid approaches for GRN construction.
To integrate prior knowledge with large-scale transcriptomic data from diverse plant species.

Main Methods:

Utilized convolutional neural networks combined with machine learning for hybrid models.
Applied transfer learning for cross-species GRN inference, especially for non-model species.
Integrated prior knowledge and transcriptomic data from Arabidopsis thaliana, poplar, and maize.

Main Results:

Hybrid models achieved over 95% accuracy, outperforming traditional methods.
Identified key regulators of lignin biosynthesis, including MYB46, MYB83, and VND, NST, SND families.
Transfer learning successfully enabled GRN inference across species with limited data.

Conclusions:

Hybrid and transfer learning approaches are highly effective for GRN prediction.
Developed a scalable framework for elucidating plant regulatory mechanisms in model and non-model species.