{
  "abstract": "Transcription factors (TFs) play an important role in regulating gene expression, making their identification a fundamental task in computational biology. In this work, we propose GraphTFactor, a novel end-to-end framework that models protein sequences as graphs and directly classifies transcription factors. By encoding sequences into graph structures, the proposed approach naturally handles variable-length inputs while preserving relevant relational information, without imposing length constraints. Experimental results show that GraphTFactor outperforms state-of-the-art methods on benchmark datasets, demonstrating the effectiveness of graph-based representations for TF prediction while providing interpretable insights into the sequence regions contributing to the model's predictions. To support reproducibility and practical adoption, we release an open-source software package and an interactive web application that let users easily perform TF prediction on custom sequences.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.40867",
  "author_total": 5,
  "authors": [
    {
      "name": "Domenico Amato"
    },
    {
      "name": "Salvatore Calderaro"
    },
    {
      "name": "Giosuè Lo Bosco"
    },
    {
      "name": "Riccardo Rizzo"
    },
    {
      "name": "Filippo Vella"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/fg822yves7gzv.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.40867",
  "fulltext_url": null,
  "id": "fg822yves7gzv",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.40867",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.AI"
    ],
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "q-bio.QM"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2610.40867",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-29",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/fg822yves7gzv.ris",
  "sources": [
    {
      "first_seen": "2026-11-02T12:21:21Z",
      "native_id": "2610.40867",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.40867"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "arxiv:q-bio.QM",
      "subfield": "bioinformatics"
    }
  ],
  "title": "GraphTFactor: A graph-based approach for proteins transcription factor prediction",
  "title_text": "GraphTFactor: A graph-based approach for proteins transcription factor prediction",
  "type": "preprint",
  "updated_at": "2026-11-04T15:20:53Z",
  "url": "https://research.draconitic.com/paper/fg822yves7gzv",
  "venue": "arXiv",
  "versions": []
}