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Revisiting K-mer Profile for Effective and Scalable Genome Representation Learning
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Revisits k-mer profile genome representations, providing theoretical analysis of their use in representation learning for metagenomic binning and genome analysis.
Problem
Effective and scalable representations of DNA sequences are needed for genome analysis, such as metagenomic binning that clusters DNA fragments by microbial origin.
Approach
Revisits simple k-mer count profiles as genome representations and provides a theoretical analysis of their use in representation learning, arguing they can be effective and scalable compared with learned deep models.
Closest papers by meaning
- What DNA Foundation Models Learn Beyond Sequence Composition (NeurIPS 2026 · similarity 0.85)
- Hyperbolic Genome Embeddings (ICLR 2025 · similarity 0.85)
- SPACE: Your Genomic Profile Predictor is a Powerful DNA Foundation Model (ICML 2025 · similarity 0.84)
- VQDNA: Unleashing the Power of Vector Quantization for Multi-Species Genomic Sequence Modeling (ICML 2024 · similarity 0.83)
- DNABERT-2: Efficient Foundation Model and Benchmark For Multi-Species Genomes (ICLR 2024 · similarity 0.83)
- Neural Embeddings for kNN Search in Biological Sequence (AAAI 2024 · similarity 0.83)
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