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About this paper
Viet Thanh Duy Nguyen, Truong-Son Hy
A comprehensive review of Protein Representation Learning (PRL) details methodologies categorized by data modality, covering feature-based, sequence-based, structure-based, multimodal, and complex-based approaches. It explores diverse applications, identifies current challenges, and proposes future research directions to advance biological and medical understanding.
Key insights
- Protein representation learning has evolved from simple feature-based methods to sophisticated deep learning models, particularly Protein Language Models and geometric deep learning, capturing rich contextual embeddings.
- Integrating multiple protein data modalities (sequence, structure, function) yields more comprehensive and accurate representations compared to single-modality approaches.
- Significant challenges in PRL include data imbalance, scalability, generalization to novel proteins, and the lack of model explainability, which require targeted future research.
Summary by alphaXiv.
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