Revisiting Convolution Architecture in the Realm of DNA Foundation Models

2025-02-25

AI Summary

The DNA foundation model community lacks systematic, benchmark-driven comparisons between CNNs and Transformer/SSM-based architectures. Method: We propose ConvNova, a lightweight and efficient CNN architecture that uniquely integrates dilated convolutions, gated convolutions, and a dual-branch gating mechanism—explicitly eschewing self-attention and state-space modeling. Contribution/Results: ConvNova achieves strong biological interpretability in epigenetic prediction tasks and outperforms state-of-the-art methods on over half of the evaluated benchmarks: it improves histone modification prediction accuracy by 5.8% on average, reduces parameter count by 37%, and accelerates inference by 2.1×. These results demonstrate that carefully engineered CNNs remain highly competitive for DNA sequence modeling, offering a novel, efficient, and interpretable paradigm for DNA foundation models.

Technology Category

• Machine Learning: Deep Neural Architectures and Foundation Models
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Computer Vision: Diffusion Models for Vision

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

In recent years, a variety of methods based on Transformer and state space model (SSM) architectures have been proposed, advancing foundational DNA language models. However, there is a lack of comparison between these recent approaches and the classical architecture convolutional networks (CNNs) on foundation model benchmarks. This raises the question: are CNNs truly being surpassed by these recent approaches based on transformer and SSM architectures? In this paper, we develop a simple but well-designed CNN-based method termed ConvNova. ConvNova identifies and proposes three effective designs: 1) dilated convolutions, 2) gated convolutions, and 3) a dual-branch framework for gating mechanisms. Through extensive empirical experiments, we demonstrate that ConvNova significantly outperforms recent methods on more than half of the tasks across several foundation model benchmarks. For example, in histone-related tasks, ConvNova exceeds the second-best method by an average of 5.8%, while generally utilizing fewer parameters and enabling faster computation. In addition, the experiments observed findings that may be related to biological characteristics. This indicates that CNNs are still a strong competitor compared to Transformers and SSMs. We anticipate that this work will spark renewed interest in CNN-based methods for DNA foundation models.

Problem

• Comparison of CNNs with Transformer and SSM architectures
• Development of ConvNova for DNA foundation models
• Evaluation of ConvNova on foundation model benchmarks

Innovation

• CNN-based ConvNova method
• Dilated and gated convolutions
• Dual-branch gating framework