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Updated: Jun 15, 2025

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Selective State Space Models Outperform Transformers at Predicting RNA-Seq Read Coverage.

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  • 1Department of Bioengineering, University of California, University Drive, Berkeley 94703.

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Summary
This summary is machine-generated.

State-space models like Mamba offer slight but consistent improvements in gene expression prediction accuracy compared to traditional transformer models. While these gains don't yet boost downstream SNP classification, Mamba-based models show promise for functional genomics.

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Area of Science:

  • Computational Biology and Genomics
  • Machine Learning in Bioinformatics

Background:

  • Transformer models are foundational for gene expression prediction from DNA, but their training is time-consuming and costly.
  • Alternative models inspired by signal processing, including state-space models (Mamba), Fourier transforms (Hyena), and wavelet transforms (MultiResNet), have emerged to address these limitations.

Purpose of the Study:

  • To evaluate alternative machine learning architectures as replacements or complements to attention mechanisms for predicting gene expression.
  • To compare the performance of convolutional, attention, Hyena, Mamba, and striped-architecture models in functional genomics tasks.

Main Methods:

  • Developed the 'bilby' software library in Python and Jax/Flax, offering various convolutional, attention, and state-space models.
  • Conducted supervised multi-task learning experiments on gene expression data.
  • Compared model performance using prediction accuracy metrics (Pearson R, r^2) and downstream SNP classification on a GTEx eQTL dataset.

Main Results:

  • Convolutional models incorporating bidirectional Mamba layers showed small but consistent prediction accuracy improvements (3-4% Pearson R, 1-2% r^2) over convolution-attention models.
  • The highest gains were observed with a striped architecture combining Mamba and attention layers.
  • Hyena models were not competitive, and MultiResNet was too slow; Mamba's accuracy gains did not significantly improve SNP classification performance.

Conclusions:

  • Selective state-space models, particularly Mamba and striped Mamba architectures, demonstrate potential for improving gene expression prediction in functional genomics.
  • Further research into Mamba-based models is warranted for these tasks, despite current limitations in downstream SNP classification.
  • The 'bilby' library and trained models are publicly available for reproducible research.