{
  "abstract": "Computational prediction of ABL1 tyrosine-kinase inhibitor (TKI) resistance is difficult because available mutation–drug datasets are small, imbalanced, and heterogeneous. This study evaluates structure-aware resistance prediction under mutation-disjoint internal validation and temporal cross-study outcome validation. The development benchmark comprised 144 ABL1 mutation–TKI records (19 resistant and 125 susceptible) labelled by a ten-fold affinity-loss criterion. A fixed panel of 11 documented structural and energetic predictors was used to minimize data-adaptive feature selection within the validation framework. A calibrated ensemble combining Elastic-Net logistic regression, radial-basis-function support-vector machines, random forests, and XGBoost was evaluated using mutation-disjoint five-fold grouped out-of-fold prediction repeated ten times. Temporal validation used a later prime-editing resistance map, yielding 84 exact mutation–TKI matches across imatinib, nilotinib, bosutinib, and ponatinib; 61 had binary Sensitive/Resistant outcomes and 23 were retained as Intermediate. The 11-feature stack achieved grouped internal AUROC 0.645 (95% CI 0.429–0.830) and PR-AUC 0.230, with mean ± SD AUROC 0.583 ± 0.101 across repeated grouped splits. On the 61 binary later-study outcomes, AUROC was 0.605 (95% CI 0.431–0.874) and PR-AUC 0.835, modestly above the resistance-prevalence baseline of 0.770. External calibration was poor (Brier score 0.583; calibration slope 0.441). Elastic-Net showed somewhat higher temporal discrimination (AUROC 0.643; PR-AUC 0.877). These results indicate limited transportability across mutation identities and experimental platforms and support use of the framework for hypothesis-generating resistance screening rather than clinically calibrated treatment selection.",
  "abstract_shown": true,
  "affiliations": [
    "Research Institute of Sciences and Engineering, University of Sharjah, Sharjah, United Arab Emirates",
    "Department of Computer Science, College of Information Technology, Misr University for Science and Technology (MUST), Giza, Egypt",
    "Faculty of Engineering, INTI International University, Nilai, Malaysia",
    "Shinawatra University, Bangkok, Pathum Thani, Thailand",
    "Applied Science Research Center, Applied Science Private University, Amman, Jordan",
    "Department of Engineering, School of Science and Engineering, Glasgow Caledonian University, Glasgow, United Kingdom",
    "Istanbul Gelisim University, Istanbul, Turkey"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Mohanad A. Deif",
      "orcid": "0000-0002-4388-1480"
    },
    {
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        2,
        3
      ],
      "name": "Mohamed A. Hafez"
    },
    {
      "affs": [
        4
      ],
      "name": "Mohammad Khishe"
    },
    {
      "affs": [
        5
      ],
      "name": "Ahmed Solyman",
      "orcid": "0000-0002-2881-8635"
    },
    {
      "affs": [
        6
      ],
      "name": "Ismail A. M. Elhaty"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4n4mzysq1rs14.bib",
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  "doi": "10.1371/journal.pone.0353101",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13712595/",
  "id": "4n4mzysq1rs14",
  "issns": [
    "1932-6203"
  ],
  "landing_url": "https://doi.org/10.1371/journal.pone.0353101",
  "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"
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      "heading",
      "Biochemistry"
    ],
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      "heading",
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    [
      "heading",
      "Biology and Life Sciences"
    ],
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      "Mutation Databases"
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    [
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      "Substitution Mutation"
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  "pdf_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0353101&type=printable",
  "pmcid": "PMC13712595",
  "pmid": "42945269",
  "published": "2026-10-27",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/4n4mzysq1rs14.ris",
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    {
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  "subjects": [
    {
      "field": "biochemistry",
      "origin": "heading:Biochemistry",
      "subfield": "general"
    },
    {
      "field": "biology",
      "origin": "plos:genetics",
      "subfield": "genetics-genomics"
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    {
      "field": "chemistry",
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    {
      "field": "mathematics",
      "origin": "heading:Probability Theory",
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    {
      "field": "medicine",
      "origin": "plos:oncology",
      "subfield": "oncology"
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  ],
  "title": "Mutation-resolved ABL1 inhibitor resistance prediction under mutation-disjoint and temporal cross-study validation",
  "title_text": "Mutation-resolved ABL1 inhibitor resistance prediction under mutation-disjoint and temporal cross-study validation",
  "type": "peer-reviewed",
  "updated_at": "2026-10-31T12:23:08Z",
  "url": "https://research.draconitic.com/paper/4n4mzysq1rs14",
  "venue": "PLOS One",
  "versions": []
}