BarcodeMAE+: Rethinking Masked Pretraining and Global Representations for DNA Barcode Foundation Models

arXiv:2609.35877 · q-bio.GN, cs.LG · Submitted 2026-09-26 · Read on arXiv

q-bio.GN, cs.LG

Submitted: 2026-09-26

Updated: 2026-09-26

Code: https://github.com/bioscan-ml/BarcodeMAE-plus

Terminology

• Accuracy — 73× in this paper · explained in 1 episode
• Foundation Model — 25× in this paper · explained in 3 episodes
• Foundation Models — 21× in this paper · explained in 7 episodes
• Transformer — 19× in this paper · explained in 2 episodes
• Embeddings — 18× in this paper · explained in 2 episodes
• Clustering — 11× in this paper · explained in 2 episodes
• Benchmark — 5× in this paper · explained in 2 episodes
• Fine-Tuning — 5× in this paper · explained in 5 episodes
• Tokenization — 5× in this paper · explained in 2 episodes
• Adaptation — 3× in this paper · explained in 1 episode
• Contrastive Learning — 3× in this paper · explained in 5 episodes
• Downstream Tasks — 3× in this paper · explained in 2 episodes
• Aggregation — 2× in this paper · explained in 1 episode
• Internal Transcribed Spacer — 2× in this paper
• Adam — 1× in this paper · explained in 2 episodes
• Adjusted Mutual Information — 1× in this paper
• Barcode Index Number — 1× in this paper
• Canadian Institute for Advanced Research — 1× in this paper
• Conference on Computer Vision and Pattern Recognition — 1× in this paper
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• In International Conference on Learning Representations — 1× in this paper
• Multimodal Dataset — 1× in this paper · explained in 2 episodes
• Perplexity — 1× in this paper · explained in 3 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Robustness — 1× in this paper · explained in 5 episodes

Sources

• Thicker and Quicker: A Jumbo Token for Fast Plain Vision Transformers
• BarcodeMamba: State Space Models for Biodiversity Analysis
• BarcodeMamba+: Advancing State-Space Models for Fungal Biodiversity Research
• An Empirical Study into Clustering of Unseen Datasets with Self-Supervised Encoders
• UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
• DINOv2: Learning Robust Visual Features without Supervision
• Enhancing DNA Foundation Models to Address Masking Inefficiencies
• Caduceus: Bi-Directional Equivariant Long-Range DNA Sequence Modeling
• DNABERT-2: Efficient Foundation Model and Benchmark For Multi-Species Genome
• DNABERT-S: Pioneering Species Differentiation with Species-Aware DNA Embeddings

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• Robust Machine Learning for Regulatory Sequence Modeling under Biological and Technical Distribution Shifts
• Stable-Shift: Biologically Structured Prediction of Transcriptional Responses to Unseen Gene Perturbations