Summary of Efficient Fine-tuning Of Single-cell Foundation Models Enables Zero-shot Molecular Perturbation Prediction, by Sepideh Maleki et al.
Efficient Fine-Tuning of Single-Cell Foundation Models Enables Zero-Shot Molecular Perturbation Prediction
by Sepideh Maleki, Jan-Christian Huetter, Kangway V. Chuang, Gabriele Scalia, Tommaso Biancalani
First submitted to arxiv on: 18 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 study proposes a novel approach to predict transcriptional responses to novel drugs using single-cell foundation models (FMs) pre-trained on vast amounts of cellular data. By introducing a drug-conditional adapter, the authors enable efficient fine-tuning of these FMs while preserving their rich biological representation. This allows not only predicting cell responses to new drugs but also generalizing to unseen cell lines without additional training. The authors establish a robust evaluation framework and demonstrate state-of-the-art results across various generalization tasks, outperforming existing baselines in few-shot and zero-shot settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study helps us understand how our cells respond to new medicines. Right now, it’s hard to predict this because there’s not enough data and the way cells work is very complex. The researchers used special models that have been trained on lots of cell information. They added a special tool that lets them adapt these models quickly to new situations. This means they can predict how cells will react to new medicines and even make predictions without any extra training. The study shows that this approach works better than others in tests, which is exciting for finding new treatments. |
Keywords
» Artificial intelligence » Few shot » Fine tuning » Generalization » Zero shot
