{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/dna2vec-consistent-vector-representations-of","title":"dna2vec: Consistent vector representations of variable-length k-mers","arxiv_id":"1701.06279","date":"2017-01-23","proceeding":null,"authors":["Patrick Ng"],"abstract":"One of the ubiquitous representation of long DNA sequence is dividing it into\nshorter k-mer components. Unfortunately, the straightforward vector encoding of\nk-mer as a one-hot vector is vulnerable to the curse of dimensionality. Worse\nyet, the distance between any pair of one-hot vectors is equidistant. This is\nparticularly problematic when applying the latest machine learning algorithms\nto solve problems in biological sequence analysis. In this paper, we propose a\nnovel method to train distributed representations of variable-length k-mers.\nOur method is based on the popular word embedding model word2vec, which is\ntrained on a shallow two-layer neural network. Our experiments provide evidence\nthat the summing of dna2vec vectors is akin to nucleotides concatenation. We\nalso demonstrate that there is correlation between Needleman-Wunsch similarity\nscore and cosine similarity of dna2vec vectors.","url_abs":"http://arxiv.org/abs/1701.06279v1","url_pdf":"http://arxiv.org/pdf/1701.06279v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"dna2vec-consistent-vector-representations-of","repo_url":"https://github.com/pnpnpn/dna2vec","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"dna2vec-consistent-vector-representations-of","repo_url":"https://github.com/DamLabResources/journalclub","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1701.06279","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1701.06279"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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