Summary of Helm: Hierarchical Encoding For Mrna Language Modeling, by Mehdi Yazdani-jahromi and Mangal Prakash and Tommaso Mansi and Artem Moskalev and Rui Liao
HELM: Hierarchical Encoding for mRNA Language Modeling
by Mehdi Yazdani-Jahromi, Mangal Prakash, Tommaso Mansi, Artem Moskalev, Rui Liao
First submitted to arxiv on: 16 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computational Engineering, Finance, and Science (cs.CE)
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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 The paper proposes a novel pre-training strategy for Language Models (LMs) called Hierarchical Encoding for mRNA Language Modeling (HELM). HELM incorporates the hierarchical nature of messenger RNA (mRNA) codon structure into language model training, which is crucial for analyzing biological sequences. The approach modulates the loss function based on codon synonymity, aligning the model’s learning process with the biological reality of mRNA sequences. The authors evaluate HELM on diverse mRNA datasets and tasks, demonstrating its superiority over standard language model pre-training and existing foundation model baselines. HELM outperforms these models by around 8% on average for seven downstream property prediction tasks and an antibody region annotation task. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about using computers to analyze special kinds of codes called messenger RNA (mRNA). These codes are important because they help make proteins in our bodies. The current way that computers analyze mRNA codes doesn’t take into account the special structure of these codes, which is important for understanding how they work. The authors developed a new way of training computer models to understand mRNA codes, which they call Hierarchical Encoding for mRNA Language Modeling (HELM). They tested their approach on different types of data and found that it worked better than other methods. |
Keywords
» Artificial intelligence » Language model » Loss function
