{
  "abstract": "Rare but biologically important molecular species are easily lost when reconstruction-based cryo-EM classification is applied to heterogeneous samples dominated by more abundant particles. We developed CryoConvNeXt, a deep-learning classifier that combines cyclic equivariance with adaptive frequency filtering. It is trained on simulated projections and adapted to experimental data by self-training, an unsupervised domain adaptation technique where the model acts as its own annotator. We tested CryoConvNeXt on cryo-EM data collected for this study from controlled binary and ternary mixtures of Catalase, Apoferritin, and HSP60. Manual particle curation provided reference labels for benchmarks with class ratios from 1:1 to 1:16. CryoConvNeXt retained minority-species recall across this range. In contrast, cryoSPARC's recall collapsed in four of six pairwise conditions, as early as 1:4 when Catalase was the minority species. By recovering low-abundance species that conventional classification fails to recover reliably, CryoConvNeXt takes an important step towards solving a central problem in quantitative visual proteomics: measuring the molecular composition of complex biological samples.",
  "abstract_shown": true,
  "affiliations": [
    "University of Basel"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "L. Glass"
    },
    {
      "name": "J. P. Abrahams"
    },
    {
      "name": "T. Braun"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7fah1nhptjnmb.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.08.750038",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.09.08.750038v1.full",
  "id": "7fah1nhptjnmb",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.09.08.750038v1",
  "language": null,
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "biophysics"
    ],
    [
      "openalex",
      "1315|Advanced Electron Microscopy Techniques and Applications"
    ]
  ],
  "openalex": "W7212152115",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.09.08.750038v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-08",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/7fah1nhptjnmb.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:56Z",
      "native_id": "10.64898/2026.09.08.750038",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.09.08.750038v1"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "biorxiv:biophysics",
      "subfield": "biophysics"
    }
  ],
  "title": "CryoConvNeXt enables robust identification of low-abundance molecular species in experimental cryo-EM data",
  "title_text": "CryoConvNeXt enables robust identification of low-abundance molecular species in experimental cryo-EM data",
  "type": "preprint",
  "updated_at": "2026-10-30T12:20:56Z",
  "url": "https://research.draconitic.com/paper/7fah1nhptjnmb",
  "venue": "bioRxiv",
  "versions": []
}