{
  "abstract": "Different fusion architectures of the quantitative Malignancy Index Diagnostic System (qMIDS) method with machine learning classifiers for oral cancer diagnosis from gene expression data are investigated. The proposed fusion architectures are tested on geographically and ethnically diverse datasets and the evaluation results showed performance improvements with late fusion, which significantly outperformed early fusion in more matched model configurations than the reverse in every cohort, although at the best-performing configuration the two architectures were statistically indistinguishable in most cohorts. Inter-dataset analysis showed robustness of the proposed architectures when the training/test samples are from different populations. Author summary: The study tests two fusion designs of the qMIDS gene-expression method combined with machine-learning classifiers for oral cancer diagnosis. Evaluations across geographically and ethnically diverse cohorts show that late fusion performs best overall, although a direct statistical comparison of the two designs shows that its advantage over early fusion, while consistent in direction, is modest and does not reach statistical significance at the best-performing setting in most cohorts. Cross-dataset experiments (training and testing on different populations) indicate the architectures are robust to population shifts.",
  "abstract_shown": true,
  "affiliations": [
    "School of Physics, Engineerng and Computer Science, University of Hertfordshire, Hatfield, United Kingdom",
    "Curenetics Ltd, London, United Kingdom",
    "Guy’s and St Thomas’ NHS Foundation Trust, London, United Kingdom",
    "Centre for Oral Immunobiology and Regenerative Medicine, Institute of Dentistry, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Ferid Ben Ali",
      "orcid": "0009-0008-9666-7251"
    },
    {
      "affs": [
        0
      ],
      "name": "Iosif Mporas"
    },
    {
      "affs": [
        0
      ],
      "name": "Vaishnavi Balaji"
    },
    {
      "affs": [
        1
      ],
      "name": "Dickson Ibaranyanakaye"
    },
    {
      "affs": [
        2
      ],
      "name": "Barbara Carey"
    },
    {
      "affs": [
        3
      ],
      "name": "Muy-Teck Teh",
      "orcid": "0000-0002-7725-8355"
    },
    {
      "affs": [
        1
      ],
      "name": "Sola Adeleke"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/03fg8qtpd375v.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pcbi.1014888",
  "fulltext_url": "https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014888",
  "id": "03fg8qtpd375v",
  "issns": [
    "1553-7358",
    "1553-734X"
  ],
  "landing_url": "https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014888",
  "language": "en",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Artificial Intelligence"
    ],
    [
      "heading",
      "Bioassays and Physiological Analysis"
    ],
    [
      "heading",
      "Biology and Life Sciences"
    ],
    [
      "heading",
      "Cancer Detection and Diagnosis"
    ],
    [
      "heading",
      "Cancers and Neoplasms"
    ],
    [
      "heading",
      "Computer and Information Sciences"
    ],
    [
      "heading",
      "Diagnostic Medicine"
    ],
    [
      "heading",
      "Engineering and Technology"
    ],
    [
      "heading",
      "Gene Expression"
    ],
    [
      "heading",
      "Genetics"
    ],
    [
      "heading",
      "Head and Neck Cancers"
    ],
    [
      "heading",
      "Machine Learning"
    ],
    [
      "heading",
      "Malignant Tumors"
    ],
    [
      "heading",
      "Medicine and Health Sciences"
    ],
    [
      "heading",
      "Microarrays"
    ],
    [
      "heading",
      "Oncology"
    ],
    [
      "heading",
      "Preprocessing"
    ],
    [
      "heading",
      "Research Article"
    ],
    [
      "heading",
      "Research and Analysis Methods"
    ],
    [
      "heading",
      "Software Engineering"
    ],
    [
      "openalex",
      "1312|Gene expression and cancer classification"
    ],
    [
      "plos",
      "/Biology and life sciences/Genetics/Gene expression"
    ],
    [
      "plos",
      "/Computer and information sciences/Artificial intelligence/Machine learning"
    ],
    [
      "plos",
      "/Computer and information sciences/Software engineering/Preprocessing"
    ],
    [
      "plos",
      "/Engineering and technology/Software engineering/Preprocessing"
    ],
    [
      "plos",
      "/Medicine and health sciences/Diagnostic medicine/Cancer detection and diagnosis"
    ],
    [
      "plos",
      "/Medicine and health sciences/Oncology/Cancer detection and diagnosis"
    ],
    [
      "plos",
      "/Medicine and health sciences/Oncology/Cancers and neoplasms"
    ],
    [
      "plos",
      "/Medicine and health sciences/Oncology/Cancers and neoplasms/Head and neck cancers"
    ],
    [
      "plos",
      "/Medicine and health sciences/Oncology/Cancers and neoplasms/Malignant tumors"
    ],
    [
      "plos",
      "/Research and analysis methods/Bioassays and physiological analysis/Microarrays"
    ]
  ],
  "openalex": "W7224114537",
  "pdf_url": "https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1014888&type=printable",
  "pmcid": "PMC13730425",
  "pmid": "42950006",
  "published": "2026-10-28",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/03fg8qtpd375v.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:17Z",
      "native_id": "10.1371/journal.pcbi.1014888",
      "source": "plos",
      "url": "https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014888"
    },
    {
      "first_seen": "2026-11-07T12:22:59Z",
      "native_id": "PMC13730425",
      "source": "pmc",
      "url": "https://doi.org/10.1371/journal.pcbi.1014888"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "heading:Gene Expression",
      "subfield": "molecular-biology"
    },
    {
      "field": "biology",
      "origin": "heading:Genetics",
      "subfield": "genetics-genomics"
    },
    {
      "field": "medicine",
      "origin": "plos:oncology",
      "subfield": "oncology"
    }
  ],
  "title": "Early vs late fusion of qMIDS with machine learning based classifiers for oral cancer diagnosis from gene expression data",
  "title_text": "Early vs late fusion of qMIDS with machine learning based classifiers for oral cancer diagnosis from gene expression data",
  "type": "peer-reviewed",
  "updated_at": "2026-11-07T12:22:59Z",
  "url": "https://research.draconitic.com/paper/03fg8qtpd375v",
  "venue": "PLOS Computational Biology",
  "versions": []
}