# dna2vec: Consistent vector representations of variable-length k-mers

arXiv: https://arxiv.org/abs/1701.06279 · HTML: https://mlsanity.com/paper/1701.06279/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jan 2017.

## Built on by (most cited first, 16 of 16)

- [Fine-Grained Zero-Shot Learning with DNA as Side Information](https://mlsanity.com/paper/2109.14133/) (2021)
- [Revisiting K-mer Profile for Effective and Scalable Genome Representation Learning](https://mlsanity.com/paper/2411.02125/) (2024)
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- [Consensus Knowledge Graph Learning via Multi-view Sparse Low Rank Block Model](https://mlsanity.com/paper/2209.13762/) (2022)
- [mRNA2vec: mRNA Embedding with Language Model in the 5'UTR-CDS for mRNA Design](https://mlsanity.com/paper/2408.09048/) (2024)
- [iEnhancer-ELM: improve enhancer identification by extracting position-related multiscale contextual information based on enhancer language models](https://mlsanity.com/paper/2212.01495/) (2022)
- [Learning distributed representations with efficient SoftMax normalization](https://mlsanity.com/paper/2303.17475/) (2023)
- [GeneMask: Fast Pretraining of Gene Sequences to Enable Few-Shot Learning](https://mlsanity.com/paper/2307.15933/) (2023)
- [Unlocking Efficiency: Adaptive Masking for Gene Transformer Models](https://mlsanity.com/paper/2408.07180/) (2024)
- [Single-Read Reconstruction for DNA Data Storage Using Transformers](https://mlsanity.com/paper/2109.05478/) (2021)
- [DNABERT-S: Pioneering Species Differentiation with Species-Aware DNA Embeddings](https://mlsanity.com/paper/2402.08777/) (2024)
- [E2Vec: Feature Embedding with Temporal Information for Analyzing Student Actions in E-Book Systems](https://mlsanity.com/paper/2407.13053/) (2024)
- [DeepHEN: quantitative prediction essential lncRNA genes and rethinking essentialities of lncRNA genes](https://mlsanity.com/paper/2309.10008/) (2023)
- [Learning Genomic Sequence Representations using Graph Neural Networks over De Bruijn Graphs](https://mlsanity.com/paper/2312.03865/) (2023)
- [Dy-mer: An Explainable DNA Sequence Representation Scheme using Dictionary Learning](https://mlsanity.com/paper/2407.12051/) (2024)
- [Leveraging Natural Language Processing to Unravel the Mystery of Life: A Review of NLP Approaches in Genomics, Transcriptomics, and Proteomics](https://mlsanity.com/paper/2506.02212/) (2025)

## Abstract (arXiv)

One of the ubiquitous representation of long DNA sequence is dividing it into shorter k-mer components. Unfortunately, the straightforward vector encoding of k-mer as a one-hot vector is vulnerable to the curse of dimensionality. Worse yet, the distance between any pair of one-hot vectors is equidistant. This is particularly problematic when applying the latest machine learning algorithms to solve problems in biological sequence analysis. In this paper, we propose a novel method to train distributed representations of variable-length k-mers. Our method is based on the popular word embedding model word2vec, which is trained on a shallow two-layer neural network. Our experiments provide evidence that the summing of dna2vec vectors is akin to nucleotides concatenation. We also demonstrate that there is correlation between Needleman-Wunsch similarity score and cosine similarity of dna2vec vectors.