{
  "abstract": "This paper predicts antimicrobial resistance (AMR) from matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectra using DRIAMS, evaluated across 13 species-antibiotic datasets spanning sensitive/resistant imbalance ratios of 2.95:1 to 141.21:1 and raw spectral similarities of 0.9166-0.9946. We propose HetSAGE, a heterogeneous graph neural network (GNN) that combines full-spectrum \"raw\" edges with consensus \"biomarker\" edges derived from a 4-method feature-selection vote and applies per-edge-type dropout to regularize the two views separately. HetSAGE outperforms a plain multilayer perceptron (MLP) only when spectral similarity is high, consistent with oversmoothing effects reported in the broader GNN literature; the biomarker view converges on features close to independently established clinical markers: within {+/-}1 Da in Staphylococcus aureus, Oxacillin, and within range in Klebsiella pneumoniae, Meropenem, confirming it is not a black box. At the same time, per-edge-type dropout protects this smaller signal from dilution by regularization tuned for the noisier raw spectrum. Together, these results indicate that heterogeneous graph structure is not a universal win for MALDI-TOF AMR prediction but a conditional one, tied to a measurable property of the data, spectral similarity, rather than to architecture alone. This caveat is largely unaddressed in current DRIAMS-based GNN work. It provides a concrete signal for practitioners deciding when a graph model is worth the added complexity over a plain MLP.",
  "abstract_shown": true,
  "affiliations": [
    "National Chung Hsing University"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "name": "W.-Y. Tsai"
    },
    {
      "affs": [
        0
      ],
      "name": "Y.- T. Shih"
    },
    {
      "name": "Y.- H. Chen"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/bqpgdedk1skwf.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.24.754085",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.09.24.754085v1.full",
  "id": "bqpgdedk1skwf",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.09.24.754085v1",
  "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",
      "1308|Bacterial Identification and Susceptibility Testing"
    ]
  ],
  "openalex": "W7215109926",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.09.24.754085v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-24",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/bqpgdedk1skwf.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:25:37Z",
      "native_id": "10.64898/2026.09.24.754085",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.09.24.754085v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "Branch-Wise Regularization for Heterogeneous GNN-Based MALDI-TOF AMR Prediction with Biomarker-Consensus Edges",
  "title_text": "Branch-Wise Regularization for Heterogeneous GNN-Based MALDI-TOF AMR Prediction with Biomarker-Consensus Edges",
  "type": "preprint",
  "updated_at": "2026-10-30T12:25:37Z",
  "url": "https://research.draconitic.com/paper/bqpgdedk1skwf",
  "venue": "bioRxiv",
  "versions": []
}