Summary of Metalic: Meta-learning In-context with Protein Language Models, by Jacob Beck et al.
Metalic: Meta-Learning In-Context with Protein Language Models
by Jacob Beck, Shikha Surana, Manus McAuliffe, Oliver Bent, Thomas D. Barrett, Juan Jose Garau Luis, Paul Duckworth
First submitted to arxiv on: 10 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 The proposed model, Metalic (Meta-Learning In-Context), leverages meta-learning to predict biophysical and functional properties of proteins. By learning over a distribution of standard fitness prediction tasks, the model demonstrates positive transfer to unseen fitness prediction tasks. When task data is available, fine-tuning enables considerable generalization, even though it’s not accounted for during meta-training. The fine-tuned models achieve strong results with 18 times fewer parameters than state-of-the-art models and set a new state-of-the-art in low-data settings on ProteinGym, an established fitness-prediction benchmark. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Metalic is a special kind of machine learning that helps predict the properties of proteins. This is important because it can help us design new proteins with specific properties. Right now, there aren’t many examples of what these proteins should look like, so we need to find ways to use existing information to make good guesses. Metalic does this by looking at lots of different tasks that are related to protein prediction and learning from those. When it doesn’t have much data, it can still do a pretty good job. This is really important because it could help us design new proteins more quickly. |
Keywords
» Artificial intelligence » Fine tuning » Generalization » Machine learning » Meta learning
