Summary of Latent Diffusion Models For Controllable Rna Sequence Generation, by Kaixuan Huang et al.
Latent Diffusion Models for Controllable RNA Sequence Generation
by Kaixuan Huang, Yukang Yang, Kaidi Fu, Yanyi Chu, Le Cong, Mengdi Wang
First submitted to arxiv on: 15 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 paper presents RNAdiffusion, a latent diffusion model that generates and optimizes discrete RNA sequences of variable lengths. The model uses pre-trained BERT-type models to encode raw RNA sequences into token-level representations, which are then compressed into fixed-length latent vectors using a Query Transformer. An autoregressive decoder is trained to reconstruct RNA sequences from these latent variables, and a continuous diffusion model is developed within this latent space. To optimize the generated RNAs, the gradients of reward models – surrogates for RNA functional properties – are integrated into the backward diffusion process. The paper shows that RNAdiffusion generates non-coding RNAs that align with natural distributions across various biological metrics, and fine-tunes the model on mRNA 5’ untranslated regions (5’-UTRs) to optimize sequences for high translation efficiencies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to generate and improve RNA sequences. It’s like a machine that can make different types of RNA molecules with specific properties. The researchers use special computer models to turn raw RNA information into useful representations, and then use those representations to create new RNA sequences. They also add a twist by using “reward” models to guide the process, so they can generate RNAs that have certain desired traits. The results show that this approach works well for creating non-coding RNAs and even optimizing specific parts of mRNA molecules for better translation efficiency. |
Keywords
* Artificial intelligence * Autoregressive * Bert * Decoder * Diffusion * Diffusion model * Latent space * Token * Transformer * Translation
