{
  "abstract": "Biological systems often exhibit intermediate molecular states under perturbation, showing changes that align with baseline condition or perturbed state. Capturing these complex patterns is critical for understanding molecular resilience and maladaptive persistence. We introduce SwitchClass, a label-switch classification framework that distinguishes features whose intermediate-state profiles align with baseline or perturbed condition. By training a dual classifier with inverted labels, SwitchClass computes a directional importance score (δ), which quantifies each feature’s alignment across biological states. Applied to colorectal cancer proteomics, SwitchClass reveals proteins that normalize after therapy and those remaining dysregulated, uncovering partial molecular recovery. In dietary perturbation and reversal phosphoproteomics, it uncovers the phosphorylation sites linked to incomplete restoration of insulin signaling. In single-cell transcriptomes from COVID-19 patients with varying severities, it identifies cell-type-specific transcripts marking resolution or persistence of inflammatory activity. Together, these analyses demonstrate SwitchClass as an interpretable framework for mapping directional molecular changes in systems with intermediate states.",
  "abstract_shown": true,
  "affiliations": [
    "Computational Systems Biology Unit, Children’s Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW 2145, Australia",
    "Sydney Precision Data Science Centre, The University of Sydney, Sydney, NSW 2006, Australia",
    "School of Mathematics and Statistics, Faculty of Science, The University of Sydney, Sydney, NSW 2006, Australia",
    "Charles Perkins Centre, The University of Sydney, Sydney, NSW 2006, Australia"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Di Xiao"
    },
    {
      "affs": [
        0
      ],
      "name": "Siqu Long"
    },
    {
      "affs": [
        0,
        1,
        2,
        3
      ],
      "name": "Pengyi Yang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/afe28tzcf2wt0.bib",
  "cited_by": 0,
  "doi": "10.1016/j.crmeth.2026.101547",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13721760/",
  "id": "afe28tzcf2wt0",
  "issns": [
    "2667-2375"
  ],
  "landing_url": "https://doi.org/10.1016/j.crmeth.2026.101547",
  "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",
      "Article"
    ],
    [
      "openalex",
      "1312|Bioinformatics and Genomic Networks"
    ]
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  "openalex": "W7202259693",
  "pdf_url": null,
  "pmcid": "PMC13721760",
  "pmid": "42586071",
  "published": "2026-08-12",
  "publisher": "Elsevier",
  "ris": "https://research.draconitic.com/paper/afe28tzcf2wt0.ris",
  "sources": [
    {
      "first_seen": "2026-11-03T12:22:24Z",
      "native_id": "PMC13721760",
      "source": "pmc",
      "url": "https://doi.org/10.1016/j.crmeth.2026.101547"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "openalex:1312|Bioinformatics and Genomic Networks",
      "subfield": "molecular-biology"
    }
  ],
  "title": "SwitchClass distinguishes baseline- and perturbation-aligned features across intermediate molecular states",
  "title_text": "SwitchClass distinguishes baseline- and perturbation-aligned features across intermediate molecular states",
  "type": "peer-reviewed",
  "updated_at": "2026-11-03T12:22:24Z",
  "url": "https://research.draconitic.com/paper/afe28tzcf2wt0",
  "venue": "Cell Reports Methods",
  "versions": []
}