Summary of Benchmarking Transcriptomics Foundation Models For Perturbation Analysis : One Pca Still Rules Them All, by Ihab Bendidi et al.
Benchmarking Transcriptomics Foundation Models for Perturbation Analysis : one PCA still rules them all
by Ihab Bendidi, Shawn Whitfield, Kian Kenyon-Dean, Hanene Ben Yedder, Yassir El Mesbahi, Emmanuel Noutahi, Alisandra K. Denton
First submitted to arxiv on: 17 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 novel evaluation framework for assessing the effectiveness of deep learning models in transcriptomics analysis, particularly for perturbation analysis. The authors develop a hierarchy of tasks for comparing the performance of pre-trained foundation models and classical techniques. A diverse set of public datasets from different sequencing techniques and cell lines are compiled to assess model performance. Results show that scVI and PCA outperform existing foundation models in understanding biological perturbations, especially in real-world scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to solve a big problem in biology: understanding how genes and compounds work together inside cells. It uses special types of computers called “deep learning” to analyze this data. But there’s a catch – the data is very noisy and hard to understand. The authors come up with a new way to test these computer models to see which ones are best at finding important patterns in this data. They use lots of public datasets from different places and cell types, and find that some models are much better than others. |
Keywords
» Artificial intelligence » Deep learning » Pca
