Artificial intelligence in bioinformatics: a survey.

Area of Science:

• Bioinformatics
• Computational Biology
• Artificial Intelligence

Background:

• Exponential growth in biological data from high-throughput sequencing and multi-omics.
• Challenges in managing, analyzing, and interpreting complex, large-scale biological datasets.
• Advancements in AI, especially deep learning and reinforcement learning, offer new analytical tools for bioinformatics.

Purpose of the Study:

• To systematically review recent progress and applications of AI in bioinformatics.
• To discuss suitable scenarios and advantages of various AI methods (traditional ML, deep learning, reinforcement learning).
• To highlight AI's transformative impact on biological research and precision medicine.

Main Methods:

• Review of recent research progress and representative applications of AI techniques in bioinformatics.
• Discussion of traditional machine learning, deep learning, and reinforcement learning methods.
• Analysis of AI advancements in specific tasks like biomedical text mining and multimodal omics integration.

Main Results:

• AI achieves landmark results: accurate protein structure prediction (0.96 Å), robust single-cell modeling (AvgBIO ≈ 0.82), high protein design success (up to 92%), and sensitive cancer detection (AUC ≈ 0.93).
• AI is applied to DNA/RNA/protein sequence analysis, 3D structure elucidation, functional annotation, multi-omics integration, and personalized drug design.
• Key challenges include data noise, sparsity, long sequence modeling, multimodal integration, interpretability, and ethical concerns.

Conclusions:

• AI significantly advances biological research and precision medicine through sophisticated data analysis.
• Future directions include large-scale data mining, cross-domain generalization, and innovations in drug design.
• An open and collaborative research ecosystem is advocated for further AI development in bioinformatics.