Progress on deep learning in genomics.

Area of Science:

• Genomics
• Bioinformatics
• Artificial Intelligence

Background:

• Genomics research faces challenges with big data generated by high-throughput sequencing.
• Traditional bioinformatics methods struggle with complex genomic data patterns.
• Deep learning offers advanced AI capabilities for data analysis and pattern recognition.

Purpose of the Study:

• To review the applications of deep learning in genomic research.
• To explore four major deep learning models: CNN, RNN, LSTM, and GAN.
• To highlight deep learning's role in livestock genomics for trait analysis, disease prevention, and genetic enhancement.

Main Methods:

• Review of deep learning models (CNN, RNN, LSTM, GAN).
• Analysis of applications in DNA, RNA, and protein research over the past five years.
• Exploration of deep learning in livestock genomics.

Main Results:

• Deep learning models demonstrate significant potential in analyzing complex genomic data.
• Applications span DNA, RNA, and protein research, improving precision and efficiency.
• Deep learning shows promise in livestock genomics for trait analysis and breeding.

Conclusions:

• Deep learning is a transformative technology for modern genomics.
• It enhances precision and efficiency in genomic research and livestock farming.
• Framework development for deep learning in livestock genomics is crucial for advancing breeding technologies.