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Artificial intelligence (AI) and machine learning (ML) are revolutionizing biology by analyzing complex genomic and proteomic data. These advanced AI tools accelerate gene function prediction, variant identification, and protein structure determination, driving personalized medicine.

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Genomics

Background:

  • Genomic and proteomic data analysis presents significant challenges due to complexity.
  • Traditional methods struggle with the scale and intricacy of biological datasets.
  • Advancements in Artificial Intelligence (AI) offer new paradigms for biological data interpretation.

Purpose of the Study:

  • To provide a comprehensive review of AI methodologies in biology.
  • To highlight AI's impact on gene function, variant identification, and protein structure prediction.
  • To discuss the integration of multi-omics data and future challenges in AI for biological research.

Main Methods:

  • Review of foundational neural networks, transformer architectures, and large language models (LLMs).
  • Analysis of AI applications in gene function prediction, genetic variant identification, and protein structure determination (e.g., AlphaFold, DeepBind).
  • Exploration of generative models for novel protein and genomic sequence design, and graph neural networks for multi-omics data fusion.

Main Results:

  • AI has revolutionized the prediction of gene function, genetic variants, and protein structures/interactions.
  • Generative AI models demonstrate unprecedented scale and accuracy in designing novel biological sequences.
  • AI-driven multi-omics data integration provides deeper insights into cellular heterogeneity and disease mechanisms, advancing personalized medicine and drug discovery.

Conclusions:

  • AI, particularly deep learning, is a transformative force in biological sciences.
  • AI facilitates breakthroughs in understanding biological systems from genes to proteins.
  • Addressing challenges in data quality, interpretability, ethics, and computation is crucial for future AI advancements in biology.