Extending Sequence Length is Not All You Need: Effective Integration of Multimodal Signals for Gene Expression Prediction
arXiv:2602.21550v1 Announce Type: new Abstract: Gene expression prediction, which predicts mRNA expression levels from DNA sequences, presents significant challenges. Previous works often focus on extending input sequence length to locate distal enhancers, which may influence target genes from hundreds of kilobases away. Our work first reveals that for current models, long sequence modeling can decrease performance. Even carefully designed algorithms only mitigate the performance degradation caused by long sequences. Instead, we find that proximal multimodal epigenomic signals near target genes prove more essential. Hence we focus on how to better integrate these signals, which has been overlooked. We find that different signal types serve distinct biological roles, with some directly marking active regulatory elements while others reflect background chromatin patterns that may introduce confounding effects. Simple concatenation may lead models to develop spurious associations with th
arXiv:2602.21550v1 Announce Type: new Abstract: Gene expression prediction, which predicts mRNA expression levels from DNA sequences, presents significant challenges. Previous works often focus on extending input sequence length to locate distal enhancers, which may influence target genes from hundreds of kilobases away. Our work first reveals that for current models, long sequence modeling can decrease performance. Even carefully designed algorithms only mitigate the performance degradation caused by long sequences. Instead, we find that proximal multimodal epigenomic signals near target genes prove more essential. Hence we focus on how to better integrate these signals, which has been overlooked. We find that different signal types serve distinct biological roles, with some directly marking active regulatory elements while others reflect background chromatin patterns that may introduce confounding effects. Simple concatenation may lead models to develop spurious associations with these background patterns. To address this challenge, we propose Prism, a framework that learns multiple combinations of high-dimensional epigenomic features to represent distinct background chromatin states and uses backdoor adjustment to mitigate confounding effects. Our experimental results demonstrate that proper modeling of multimodal epigenomic signals achieves state-of-the-art performance using only short sequences for gene expression prediction.
Executive Summary
This article challenges the conventional approach to gene expression prediction by extending sequence length, instead highlighting the importance of integrating multimodal epigenomic signals. The authors propose Prism, a framework that effectively models these signals to achieve state-of-the-art performance using short sequences. This approach mitigates confounding effects and demonstrates that proximal signals near target genes are more crucial than long sequence modeling. The study's findings have significant implications for gene expression prediction and the development of more accurate models.
Key Points
- ▸ Long sequence modeling can decrease performance in gene expression prediction
- ▸ Proximal multimodal epigenomic signals are more essential for accurate prediction
- ▸ The proposed Prism framework effectively integrates these signals to achieve state-of-the-art performance
Merits
Innovative Approach
The article presents a novel framework for integrating multimodal epigenomic signals, which overcomes the limitations of traditional sequence-based approaches.
Demerits
Limited Generalizability
The study's findings may not be generalizable to all types of gene expression prediction tasks, and further research is needed to validate the results.
Expert Commentary
The article presents a significant shift in the field of gene expression prediction, highlighting the importance of multimodal epigenomic signals and the limitations of traditional sequence-based approaches. The proposed Prism framework demonstrates the potential for more accurate models, and its implications for our understanding of gene regulation and disease are substantial. However, further research is needed to fully realize the potential of this approach and to address the challenges of generalizability and confounding effects.
Recommendations
- ✓ Further research should focus on validating the Prism framework across diverse datasets and gene expression prediction tasks
- ✓ The development of more sophisticated models that can effectively integrate multimodal epigenomic signals and account for confounding effects is a critical area for future research.
