HELM: Hierarchical Encoding for mRNA Language Modeling
Mehdi Yazdani-Jahromi, Mangal Prakash, Tommaso Mansi, Artem, Moskalev, Rui Liao
TL;DR
HELM introduces a hierarchical encoding strategy for mRNA language modeling that incorporates codon structure, improving predictive accuracy and generative diversity over standard models.
Contribution
The paper presents a novel hierarchical pre-training approach that explicitly models mRNA codon structure, enhancing biological relevance and performance.
Findings
Outperforms standard language models on seven downstream tasks.
Improves antibody region annotation accuracy.
Generates more biologically plausible mRNA sequences.
Abstract
Messenger RNA (mRNA) plays a crucial role in protein synthesis, with its codon structure directly impacting biological properties. While Language Models (LMs) have shown promise in analyzing biological sequences, existing approaches fail to account for the hierarchical nature of mRNA's codon structure. We introduce Hierarchical Encoding for mRNA Language Modeling (HELM), a novel pre-training strategy that incorporates codon-level hierarchical structure into language model training. HELM modulates the loss function based on codon synonymity, aligning the model's learning process with the biological reality of mRNA sequences. We evaluate HELM on diverse mRNA datasets and tasks, demonstrating that HELM outperforms standard language model pre-training as well as existing foundation model baselines on seven diverse downstream property prediction tasks and an antibody region annotation tasks…
Peer Reviews
Decision·ICLR 2025 Poster
1. The authors combine biological insight with language modeling. 2. The proposed HELM is solid and performs well for mRNA-related tasks. It achieves SOTA performance on several practical and impactful tasks, e.g., mRNA sequence design, and sequence region annotation. 3. The authors provide a curated dataset, and curated domain knowledge, which may benefit future computation-oriented research. 4. The authors provide solid benchmark experiments of various backbone architectures and tokenization m
1. The methodology itself is relatively simple. 2. It is not clear to what extent the model, the codes, and the data will be made public.
1. This study proposed a novel approach for embedding biological hierarchical structures into language models to enhance interpretability and accuracy in biological sequence analysis. 2. A comprehensive technical evaluation spanning multiple model architectures was presented, benchmarked across diverse datasets and use cases. 3. Clear and precise presentation of technical methods and experimental setup were provided to facilitate reproducibility and further research.
1. The performance improvement seems more attributable to data selection than methodology: - HELM uses antibody mRNA while baselines use different data types (ncRNA, pre-mRNA, diverse organisms mRNA). 2. The paper fails to justify why antibody mRNA pre-training would generalize to: - Viral RNA sequences - Riboswitch sequences. 3. The evaluation methodology is insufficient: a. Over-reliance on single metric (Spearman correlation) b. No analysis across sequence lengths c. No biological interpret
1. Biological Prior Integration: The hierarchical encoding strategy effectively incorporates biological knowledge of mRNA structure, particularly the synonymous codon usage, which enhances the model’s performance on property prediction and generative tasks. 2. Diverse Evaluations: HELM's performance is thoroughly evaluated across multiple tasks, including property prediction, generative sequence design, and antibody sequence region annotation, showcasing the model’s versatility and relevance to
1. Lack of Scaling Experiments: The paper primarily employs models with 50M parameters, which is relatively small compared to large language models (LLMs) in NLP. This limitation raises concerns about the necessity of HELM’s hierarchical encoding, as larger models might naturally learn these hierarchical relationships without explicit design. Experiments with larger models could help clarify if the hierarchical loss function offers unique advantages or if it becomes redundant with increased scal
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Taxonomy
TopicsNatural Language Processing Techniques
MethodsALIGN