{
  "abstract": "Generative modeling of genomic sequences presents a stringent test for deep learning, requiring the capture of long-range dependencies and functional constraints beyond local nucleotide statistics. Existing architectures frequently collapse to limited modes or reproduce shallow nucleotide distributions without encoding functional semantics. We introduce the Multi-Task Adversarial Autoencoder (MT-AAE), a hybrid generative framework that integrates adversarial regularization with auxiliary functional and biophysical objectives to enforce structured latent representations. Evaluated on an empirical human gene corpus, MT-AAE achieved a Train-on-Synthetic-Test-on-Real (TRTS) accuracy of 74.7%, compared with 41.0% for a standard GAN baseline. Stratified analysis further showed that functional discriminability increased to 89.3% when sequence lengths aligned with the model’s architectural window. Importantly, the learned representations exhibited emergent biological structure: synthetic sequences spontaneously preserved cis-regulatory syntax, including canonical TATA-box motifs recovered across 100% of generated promoter sequences without explicit rule encoding, though positional placement relative to the TSS was not statistically significant (KS p=0.90), and the high occurrence rate is partly attributable to the AT-rich composition of the generated sequences. Representation-level validation using frozen DNABERT-2 and DNABERT-S embeddings confirmed that the generated sequences retained functional information beyond shallow k-mer statistics. Cross-species evaluation on Mus musculus sequences further demonstrated species-specific learning consistent with known human–mouse regulatory divergence. The framework also mitigated mode collapse, maintaining near-uniform generation across functional classes (\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$R_g \\approx 1.0$$\\end{document}), including rare categories such as tRNAs ( < 2% of the dataset). These findings position MT-AAE as an effective framework for biologically constrained genomic sequence generation.\n\nSupplementary Information: The online version contains supplementary material available at https://doi.org/10.1186/s13040-026-00591-9.",
  "abstract_shown": true,
  "affiliations": [
    "Computer Information Science Department, Universiti Teknologi PETRONAS, Seri Iskandar, Malaysia. shamsu200@yahoo.com.",
    "IAIICT, Ahmadu Bello University, Zaria, Nigeria. shamsu200@yahoo.com.",
    "Computer Information Science Department, Universiti Teknologi PETRONAS, Seri Iskandar, Malaysia. seddig.alhussian@utp.edu.my.",
    "Computer Information Science Department, Universiti Teknologi PETRONAS, Seri Iskandar, Malaysia.",
    "King Khalid University, Applied College of Muhayil Asir, Abha, Saudi Arabia.",
    "Department of Information Systems, College of Economics, Management and Information Systems, University of Nizwa, Al Khuwair, Oman.",
    "IAIICT, Ahmadu Bello University, Zaria, Nigeria."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 11,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Shamsuddeen Adamu",
      "orcid": "0009-0008-4015-1723"
    },
    {
      "affs": [
        2
      ],
      "name": "Hitham Alhussian"
    },
    {
      "affs": [
        3
      ],
      "name": "Said Jadid Abdulkadir"
    },
    {
      "affs": [
        4
      ],
      "name": "Majdy Mohamed Eltayeb Eltahir"
    },
    {
      "affs": [
        5
      ],
      "name": "Sallam O F Khairy"
    },
    {
      "affs": [
        3,
        6
      ],
      "name": "Ibrahim Hayatu Hassan"
    },
    {
      "affs": [
        3
      ],
      "name": "Ibrahim Muhammad Kurah"
    },
    {
      "affs": [
        3
      ],
      "name": "Abubakar Mukhtar"
    },
    {
      "affs": [
        3
      ],
      "name": "Vijayakanthan Ganesalingam"
    },
    {
      "affs": [
        3
      ],
      "name": "Vaishali Ravi"
    },
    {
      "affs": [
        3
      ],
      "name": "Yahaya Saidu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/2m2wacvggw7cg.bib",
  "cited_by": 0,
  "doi": "10.1186/s13040-026-00591-9",
  "fulltext_url": "https://europepmc.org/articles/PMC13726178",
  "id": "2m2wacvggw7cg",
  "issns": [
    "1756-0381"
  ],
  "landing_url": "https://europepmc.org/article/MED/42968110",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Research"
    ],
    [
      "openalex",
      "1312|Genomics and Chromatin Dynamics"
    ]
  ],
  "openalex": "W7172331645",
  "pdf_url": "https://europepmc.org/articles/PMC13726178?pdf=render",
  "pmcid": "PMC13726178",
  "pmid": "42968110",
  "published": "2026-08-03",
  "publisher": "BMC",
  "ris": "https://research.draconitic.com/paper/2m2wacvggw7cg.ris",
  "sources": [
    {
      "first_seen": "2026-11-03T15:20:54Z",
      "native_id": "MED:42968110",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42968110"
    },
    {
      "first_seen": "2026-11-05T12:22:35Z",
      "native_id": "PMC13726178",
      "source": "pmc",
      "url": "https://doi.org/10.1186/s13040-026-00591-9"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "openalex:1312|Genomics and Chromatin Dynamics",
      "subfield": "molecular-biology"
    }
  ],
  "title": "Multi-task adversarial autoencoder for functional genomic element generation with preserved biophysical properties.",
  "title_text": "Multi-task adversarial autoencoder for functional genomic element generation with preserved biophysical properties.",
  "type": "peer-reviewed",
  "updated_at": "2026-11-06T12:22:12Z",
  "url": "https://research.draconitic.com/paper/2m2wacvggw7cg",
  "venue": "BioData mining",
  "versions": []
}