{
  "abstract": "Partially observed object-by-context matrices arise across data-rich science, where dominant object effects can obscure smaller but informative context-dependent variation. We study this problem in a translation-efficiency atlas of 9,494 5′ UTRs across 78 cellular and tissue contexts. We present DisenTE, a sequence-conditioned neural model that combines separate sequence and context branches with a sparse low-rank pattern–context channel. Each module pairs a sequence-derived activation with context-specific deployment weights, forming a dictionary whose sequence and context components can be examined separately. Under five-fold within-panel entry masking, DisenTE achieves a UTR-centered residual Spear-man correlation of 0.641±0.005, compared with 0.304±0.003 for the strongest reference model. The learned dictionary retains 11 of 20 candidate modules. CTM 6 has the largest overlap with an external TOP set and a cap-proximal pyrimidine pattern; CTMs 5 and 7 also overlap the set but have purine-containing consensuses. The evidence supports CTM 6 as a TOP sequence anchor and CTMs 5 and 7 as TOP-set-associated factors. On this dataset, DisenTE improves completion over the evaluated references and provides module-level summaries of its fitted context-dependent variation.",
  "abstract_shown": true,
  "affiliations": [
    "City University of Hong Kong"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "name": "M. Huo"
    },
    {
      "name": "Y. Yu"
    },
    {
      "affs": [
        0
      ],
      "name": "S. Li"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/brd526p005pr8.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.23.753721",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.09.23.753721v1.full",
  "id": "brd526p005pr8",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.09.23.753721v1",
  "language": null,
  "licence": "PDM-1.0",
  "licence_label": "Public domain",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/publicdomain/mark/1.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "bioinformatics"
    ],
    [
      "openalex",
      "1312|RNA and protein synthesis mechanisms"
    ]
  ],
  "openalex": "W7214715601",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.09.23.753721v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-23",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/brd526p005pr8.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:25:13Z",
      "native_id": "10.64898/2026.09.23.753721",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.09.23.753721v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "DisenTE: Sparse Pattern-Context Modeling for Interpretable Translation-Efficiency Matrix Completion",
  "title_text": "DisenTE: Sparse Pattern-Context Modeling for Interpretable Translation-Efficiency Matrix Completion",
  "type": "preprint",
  "updated_at": "2026-10-30T12:25:13Z",
  "url": "https://research.draconitic.com/paper/brd526p005pr8",
  "venue": "bioRxiv",
  "versions": []
}