{
  "abstract": "Protein protein interaction (PPI) predictors are often evaluated with random pair splits that permit proteins to recur across training and test sets, while constructed negative pairs can introduce a separate source of benchmark-specific signal. We reassessed the Guo yeast benchmark using a matched protein novelty curve, independently generated MMseqs2 sequence cluster controlled R3 variants, topology only controls, degree- and length matched putative negatives, handcrafted and frozen protein language model representations, multiple model configurations, seed analyses, and connected component aware inference. In the matched R1[->]R2[->]R3 comparison, AAC+CTD-MLP ROC-AUC declined from 0.697 for seen-seen pairs to 0.626 for one-unseen pairs and 0.587 for both-unseen pairs. Across independently regenerated cluster-disjoint R3 variants at 50%, 40%, 30%, and 20% MMseqs2 clustering thresholds, performance showed no consistent monotonic trend. Guo original-negative endpoints had lower positive-network degree than positive endpoints (mean 3.34 versus 6.72), and a degree-only logistic model reached ROC-AUC 0.962 on the original negatives but 0.502 on degree-matched negatives. We used degree-matched negatives consistently for R3 training and testing; six sequence based model configurations yielded ROC-AUC 0.493- 0.547; none showed corrected connected-component-aware evidence of above-chance discrimination. A PIPR multispecies analysis reproduced degree-associated label separation but also exposed cross-species pairing as an additional pooled cue. These results show that protein novelty, operational sequence cluster separation, and negative-set topology are distinct dimensions of PPI benchmark evaluation. The Guo result is benchmark-specific and should not be generalized to sequence-based PPI prediction or protein language models overall. The dataset and source code for the experiments are publicly available at https://github.com/morufwork/guoAudit.git.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Computer Science, Universidade Federal de Vicosa"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "M. A. Adeagbo"
    },
    {
      "name": "H. A. Agbaje"
    },
    {
      "name": "S. A. Silveira"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/av0syhp20r7gw.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.06.757125",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.10.06.757125v1.full",
  "id": "av0syhp20r7gw",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.10.06.757125v1",
  "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",
      "1312|Bioinformatics and Genomic Networks"
    ]
  ],
  "openalex": "W7221782100",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.10.06.757125v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-06",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/av0syhp20r7gw.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:27:51Z",
      "native_id": "10.64898/2026.10.06.757125",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.10.06.757125v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "Protein novelty, sequence similarity, and negative sampling controls reveal benchmark specific discrimination in protein protein interactionprediction",
  "title_text": "Protein novelty, sequence similarity, and negative sampling controls reveal benchmark specific discrimination in protein protein interactionprediction",
  "type": "preprint",
  "updated_at": "2026-10-30T12:27:51Z",
  "url": "https://research.draconitic.com/paper/av0syhp20r7gw",
  "venue": "bioRxiv",
  "versions": []
}