{
  "abstract": "Baseline transcriptomes can predict anticancer drug response, but their high dimensionality complicates pathway-level interpretation. We developed a probabilistic representation learning framework using a reusable variational autoencoder (VAE) latent space to prioritize response-associated factors by aggregate importance and drug preferentiality. Across five reference drugs, leakage-free repeated cross-validation showed that gene-level Ridge regression performed best, whereas 64-dimensional latent representations retained predictive signal after compression. In a separate interpretation VAE fitted to 939 cancer cell lines, six aggregate-importance candidates were bootstrap-robust within the fixed representation and were associated with immune/inflammatory, oncogenic, cell-cycle, stress-response, and cellular-processing programs. Drug-preferential candidates lacked factor-level bootstrap robustness, although several pathway associations remained supported after lineage adjustment. Five independent VAE refits showed partial gene-program concordance but seed-sensitive axes, while a principal component analysis baseline supported stable factor selection and pathway annotation. As an exploratory application, baseline transcriptomes from 15 QAL333-tested cell lines were projected into the fixed VAE without encoder retraining. QAL333-prioritized factors were associated with inflammatory, oncogenic, cellular-processing, and microenvironment-related pathways. The two-model zebrafish xenograft comparison remained descriptive and hypothesis-generating. These findings support reusable, stability-aware latent-space analysis as a framework for pathway-level hypothesis generation from high-dimensional transcriptomic drug-response data.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "name": "Hyon Hee Kim"
    },
    {
      "name": "Suji Im"
    },
    {
      "name": "Sumin Kim"
    },
    {
      "name": "Jiyeon Han"
    },
    {
      "name": "Jiyun Kim"
    },
    {
      "name": "Jaehyun Sim"
    },
    {
      "name": "Seyeon Park"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4ng2dn6d1jq9j.bib",
  "cited_by": 0,
  "doi": "10.1038/s41598-026-77428-2",
  "fulltext_url": null,
  "id": "4ng2dn6d1jq9j",
  "issns": [
    "2045-2322"
  ],
  "landing_url": "https://www.nature.com/articles/s41598-026-77428-2",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [],
  "openalex": null,
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-30",
  "publisher": "Springer Science and Business Media LLC",
  "ris": "https://research.draconitic.com/paper/4ng2dn6d1jq9j.ris",
  "sources": [
    {
      "first_seen": "2026-10-31T00:20:32Z",
      "native_id": "10.1038/s41598-026-77428-2",
      "source": "crossref",
      "url": "https://www.nature.com/articles/s41598-026-77428-2"
    }
  ],
  "subjects": [],
  "title": "Probabilistic representation learning of baseline transcriptomes enables prediction and pathway-level interpretation of anticancer drug responses",
  "title_text": "Probabilistic representation learning of baseline transcriptomes enables prediction and pathway-level interpretation of anticancer drug responses",
  "type": "peer-reviewed",
  "updated_at": "2026-11-01T00:20:29Z",
  "url": "https://research.draconitic.com/paper/4ng2dn6d1jq9j",
  "venue": "Scientific Reports",
  "versions": []
}