{
  "abstract": "Effective and high-quality gene function representations are crucial for bioinformatics tasks. While biological knowledge about genes is inherently textual, encompassing functional descriptions and pathway annotations, current methods often rely on single-source, non-textual data, failing to capture the rich semantic information in multi-source gene annotations. In this paper, we propose GeneEmbedLLM, a Large Language Model (LLM)-powered framework for generating universal, semantically-aware gene embeddings. The proposed approach advances the gene representation learning through multi-source structured text integration from five authoritative databases and attribute-aware attention based on Longformer architecture and contrastive learning. Experimental results on diverse downstream tasks, ranging from molecular interactions and phenotypic dependencies to epigenetic regulation, confirm the validity and effectiveness of our proposed approach, where competitive performance is achieved across all evaluated benchmarks. We further validate the quality of the generated embeddings through comprehensive analyses, including embedding visualizations, intra-pathway similarity assessments, ablation study, and parameter analysis.",
  "abstract_shown": true,
  "affiliations": [
    "Guangzhou National Laboratory"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "name": "z. zhang"
    },
    {
      "name": "l. yang"
    },
    {
      "name": "z. he"
    },
    {
      "name": "j. ding"
    },
    {
      "name": "Y. Li"
    },
    {
      "affs": [
        0
      ],
      "name": "R. Shen"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4hjz7x42p9061.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.11.05.764758",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.11.05.764758v1.full",
  "id": "4hjz7x42p9061",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.11.05.764758v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "bioinformatics"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.11.05.764758v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-05",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/4hjz7x42p9061.ris",
  "sources": [
    {
      "first_seen": "2026-11-10T00:23:31Z",
      "native_id": "10.64898/2026.11.05.764758",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.11.05.764758v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "GeneEmbedLLM: Harnessing Large Language Models for Gene Functional Embedding and Association Prediction",
  "title_text": "GeneEmbedLLM: Harnessing Large Language Models for Gene Functional Embedding and Association Prediction",
  "type": "preprint",
  "updated_at": "2026-11-10T00:23:31Z",
  "url": "https://research.draconitic.com/paper/4hjz7x42p9061",
  "venue": "bioRxiv",
  "versions": []
}