{
  "abstract": "Adverse drug reaction (ADR) prioritization and ordinal-frequency estimation are related but distinct prediction objectives, while aggregate ranking performance in sparse annotation matrices can be strongly influenced by frequently annotated side effects. Within CAFNet-DG, CAFNet-D uses task-specific association and ordinal-frequency functions built on shared molecular-relational representations, while annotation prevalence is evaluated explicitly within each training fold. On a SIDER-derived dataset comprising 750 drugs and 994 side effects, replacing a shared output with task-specific functions produced the largest joint change in staged drug-disjoint ablation, increasing mAP from 0.340 to 0.382 and reducing RMSE from 1.754 to 1.142. A prevalence-only ranking achieved an mAP of 0.410, close to the CAFNet-DG value of 0.413, indicating that aggregate ADR ranking is strongly associated with the benchmark annotation distribution. Prevalence-matched, prevalence-stratified, frequently annotated side-effect removal, and per-drug analyses were therefore used to distinguish drug-conditioned ranking variation from global annotation-prevalence effects. An equal-weight same-architecture ensemble control yielded no significant differences from CAFNet-DG across seven ranking metrics after Holm correction; generic variance reduction therefore could not be excluded as an explanation for the modest score-integration gain. Scaffold-disjoint and independent-data evaluations showed modest transfer, while ordinal-frequency agreement on CT-ADE remained limited. The results support the CAFNet-D task-specific formulation and prevalence-aware evaluation, while CAFNet-DG provides the score-integration setting examined under matched ensemble controls.",
  "abstract_shown": true,
  "affiliations": [
    "College of Electronic Information, Hunan First Normal University, Changsha 410205, China.",
    "School of Computer Science and Technology, Changsha University of Science and Technology, Changsha 410114, China."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Wen Li",
      "orcid": "0000-0002-0197-3432"
    },
    {
      "affs": [
        0
      ],
      "name": "Zhiyu Xu"
    },
    {
      "affs": [
        0
      ],
      "name": "Xiangqiong Wu",
      "orcid": "0000-0001-7577-0534"
    },
    {
      "affs": [
        1
      ],
      "name": "Ju Xiang"
    },
    {
      "affs": [
        0
      ],
      "name": "Xiaojie Zhang",
      "orcid": "0009-0005-6576-1876"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/cmn6nk8mzmp50.bib",
  "cited_by": 0,
  "doi": "10.3390/ijms27198685",
  "fulltext_url": "https://europepmc.org/articles/PMC13665361",
  "id": "cmn6nk8mzmp50",
  "issns": [
    "1422-0067"
  ],
  "landing_url": "https://europepmc.org/article/MED/42883930",
  "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": [
    [
      "openalex",
      "1703|Computational Drug Discovery Methods"
    ]
  ],
  "openalex": "W7214648308",
  "pdf_url": "https://europepmc.org/articles/PMC13665361?pdf=render",
  "pmcid": "PMC13665361",
  "pmid": "42883930",
  "published": "2026-09-28",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/cmn6nk8mzmp50.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:16Z",
      "native_id": "MED:42883930",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42883930"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "issn:1422-0067",
      "subfield": "general"
    }
  ],
  "title": "CAFNet-DG: Task-Specific Modeling and Prevalence-Aware Evaluation for Adverse Drug Reaction Prioritization with Ordinal-Frequency Estimation.",
  "title_text": "CAFNet-DG: Task-Specific Modeling and Prevalence-Aware Evaluation for Adverse Drug Reaction Prioritization with Ordinal-Frequency Estimation.",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:19:16Z",
  "url": "https://research.draconitic.com/paper/cmn6nk8mzmp50",
  "venue": "International journal of molecular sciences",
  "versions": []
}