# Revisiting K-mer Profile for Effective and Scalable Genome Representation Learning

arXiv: https://arxiv.org/abs/2411.02125 · HTML: https://mlsanity.com/paper/2411.02125/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 34 references that are in the map.
- First posted on arXiv 4 Nov 2024.

## Builds on (most cited first, 5 of 5)

- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [HyenaDNA: Long-Range Genomic Sequence Modeling at Single Nucleotide Resolution](https://mlsanity.com/paper/2306.15794/) (2023)
- [DNABERT-2: Efficient Foundation Model and Benchmark For Multi-Species Genome](https://mlsanity.com/paper/2306.15006/) (2023)
- [dna2vec: Consistent vector representations of variable-length k-mers](https://mlsanity.com/paper/1701.06279/) (2017)
- [Multiple Kernel Representation Learning on Networks](https://mlsanity.com/paper/2106.05057/) (2021)

## Built on by (most cited first, 6 of 6)

- [SPACE: Your Genomic Profile Predictor is a Powerful DNA Foundation Model](https://mlsanity.com/paper/2506.01833/) (2025)
- [UncertainGen: Uncertainty-Aware Representations of DNA Sequences for Metagenomic Binning](https://mlsanity.com/paper/2509.26116/) (2025)
- [Context-Aware Regularization with Markovian Integration for Attention-Based Nucleotide Analysis](https://mlsanity.com/paper/2507.09378/) (2025)
- [Mixing Configurations for Downstream Prediction](https://mlsanity.com/paper/2510.19248/) (2025)
- [Contrastive Linearized Laplace: Loss Geometry, Valid Repairs, and Reliable Cores](https://mlsanity.com/paper/2610.14875/) (2026)
- [What DNA Foundation Models Learn Beyond Sequence Composition](https://mlsanity.com/paper/2610.15711/) (2026)

## Abstract (arXiv)

Obtaining effective representations of DNA sequences is crucial for genome analysis. Metagenomic binning, for instance, relies on genome representations to cluster complex mixtures of DNA fragments from biological samples with the aim of determining their microbial compositions. In this paper, we revisit k-mer-based representations of genomes and provide a theoretical analysis of their use in representation learning. Based on the analysis, we propose a lightweight and scalable model for performing metagenomic binning at the genome read level, relying only on the k-mer compositions of the DNA fragments. We compare the model to recent genome foundation models and demonstrate that while the models are comparable in performance, the proposed model is significantly more effective in terms of scalability, a crucial aspect for performing metagenomic binning of real-world datasets.