Summary of Clustering For Protein Representation Learning, by Ruijie Quan et al.
Clustering for Protein Representation Learning
by Ruijie Quan, Wenguan Wang, Fan Ma, Hehe Fan, Yi Yang
First submitted to arxiv on: 30 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computational Engineering, Finance, and Science (cs.CE); Biomolecules (q-bio.BM); Quantitative Methods (q-bio.QM)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a neural clustering framework for protein representation learning, which captures the structure and function of proteins from their amino acid sequences. The approach addresses the challenge that previous methods ignored the unequal importance of amino acids in protein folding and activity. The framework treats a protein as a graph, where nodes represent amino acids and edges represent spatial or sequential connections. An iterative clustering strategy groups nodes into clusters based on 1D and 3D positions, with scores assigned to each cluster. The highest-scoring clusters are selected for the next iteration until a hierarchical representation is obtained. The method achieves state-of-the-art performance on four protein-related tasks: fold classification, enzyme reaction classification, gene ontology term prediction, and enzyme commission number prediction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand proteins by creating a special kind of map that shows how important each part of the protein is. This is helpful because not all parts of the protein are equal in terms of its function. The researchers use a new way to group these parts together, called neural clustering, which looks at both the order and position of the amino acids. They test their method on several tasks related to proteins, like predicting what an enzyme will do or what a gene is responsible for. Their results are the best so far. |
Keywords
» Artificial intelligence » Classification » Clustering » Representation learning
