Summary of Training on Test Proteins Improves Fitness, Structure, and Function Prediction, by Anton Bushuiev et al.
Training on test proteins improves fitness, structure, and function prediction
by Anton Bushuiev, Roman Bushuiev, Nikola Zadorozhny, Raman Samusevich, Hannes Stärk, Jiri Sedlar, Tomáš Pluskal, Josef Sivic
First submitted to arxiv on: 4 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Biomolecules (q-bio.BM)
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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 research paper proposes a novel approach to enhance machine learning model generalization when applied to biological data, particularly proteins. The method, called self-supervised fine-tuning at test time (TTT), allows models to adapt to specific proteins without requiring additional training data. By minimizing perplexity and leveraging the power of self-supervised pre-training, TTT consistently improves generalization across various models, scales, and datasets. Notably, the authors demonstrate state-of-the-art results on protein fitness prediction, enhanced structure prediction for challenging targets, and improved function prediction accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to teach a machine learning model to recognize different proteins in biology. It’s like trying to teach a child to recognize different animals, but the child has never seen most of them before! This paper shows how to help machines learn about individual proteins without needing more data. They introduce a new way to fine-tune the models as they make predictions, allowing them to get better at recognizing specific proteins. This leads to better results in predicting protein fitness, structure, and function. |
Keywords
» Artificial intelligence » Fine tuning » Generalization » Machine learning » Perplexity » Self supervised
