Summary of Multi-modal Transfer Learning Between Biological Foundation Models, by Juan Jose Garau-luis et al.
Multi-modal Transfer Learning between Biological Foundation Models
by Juan Jose Garau-Luis, Patrick Bordes, Liam Gonzalez, Masa Roller, Bernardo P. de Almeida, Lorenz Hexemer, Christopher Blum, Stefan Laurent, Jan Grzegorzewski, Maren Lang, Thomas Pierrot, Guillaume Richard
First submitted to arxiv on: 20 Jun 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 This paper proposes a novel multi-modal model that connects DNA, RNA, and protein sequences by leveraging information from different modality-specific encoders. The approach is designed to address key problems in genomics that involve multiple sequence modalities. Specifically, the authors develop IsoFormer, a model that predicts how multiple RNA transcript isoforms originate from the same gene and map to different transcription expression levels across various human tissues. IsoFormer outperforms existing methods by leveraging the use of multiple modalities and achieving efficient transfer knowledge between encoders. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to understand how genes work in our bodies. It’s like trying to figure out a puzzle, where you have different pieces that need to fit together correctly. The authors are using special computer models to connect DNA, RNA, and proteins into one system. This will help scientists better understand diseases and develop new treatments. They’re testing this approach on a tricky problem: figuring out how different types of RNA molecules come from the same gene in our bodies. So far, their model is doing a great job of solving this puzzle! |
Keywords
* Artificial intelligence * Multi modal
