{
  "abstract": "Models that predict RNA abundance from chromatin accessibility are rarely compared under the same features, targets and metrics. We built one benchmark on 126,604 paired single-nucleus profiles from 19 human studies with a shared vocabulary of 50,000 peaks and 3,185 genes, ten 5x2 cross-validation folds and one scoring target, and compared seven implemented pipelines and two reference predictors across 70 trained runs; each pipeline keeps its own input normalisation and native training target. The scButterfly-B pipeline, built on dual variational autoencoders, outperformed every transformer pipeline, including an autoregressive Perceiver model (Omic Translator) and a support-free reformulation of Cisformer, on per-gene and within-cell Spearman correlation in all sixty paired fold comparisons, with 3.8 times shorter training. Transformer pipelines come within 0.02-0.04 on within-cell profile shape but lag by 0.10-0.24 on per-gene correlation. On two withheld cell types per-gene correlation is low; controls show the podocyte values are typical of within-type correlation for seen cell types of the same size, whereas Paneth values match a cell-agnostic shuffle for every Omic Translator arm. Within the transformer family, prespecified ablations show the Perceiver encoder was worth +0.0439 in within-donor per-gene correlation over full self-attention, a chromosome-aware genomic attention prior reduced it by 0.0096 (exact donor-level sign-flip test, Holm-adjusted p = 1.2e-4), and decoder output order had no detectable effect. A training-mean profile reaches 0.314 within-cell correlation with no input and a constant at the true expressed-gene positions reaches 0.996 per-gene correlation, so oracle-support protocols are reported only as diagnostics. Code, results and an audited release are provided.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.28853",
  "author_total": 1,
  "authors": [
    {
      "name": "Jesse G. Meyer"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/dsscqzncsm29k.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.28853",
  "fulltext_url": null,
  "id": "dsscqzncsm29k",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.28853",
  "language": null,
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "q-bio.GN"
    ],
    [
      "openalex",
      "1312|Genomics and Chromatin Dynamics"
    ]
  ],
  "openalex": "W7223244588",
  "pdf_url": "https://arxiv.org/pdf/2610.28853",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-20",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/dsscqzncsm29k.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:34:23Z",
      "native_id": "2610.28853",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.28853"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "arxiv:q-bio.GN",
      "subfield": "genetics-genomics"
    }
  ],
  "title": "An autoencoder outperforms two transformers on prediction of gene expression from chromatin accessibility",
  "title_text": "An autoencoder outperforms two transformers on prediction of gene expression from chromatin accessibility",
  "type": "preprint",
  "updated_at": "2026-10-30T12:34:23Z",
  "url": "https://research.draconitic.com/paper/dsscqzncsm29k",
  "venue": "arXiv",
  "versions": []
}