{
  "abstract": "Drug mechanism-of-action (MoA) modeling commonly relies on perturbational transcriptomes, but matched microRNA (miRNA) measurements are often unavailable. Inferred regulatory features offer a scalable way to reuse these data. Here, we present MIRCID, a framework comparing gene expression with inferred transcription factor (TF) activity and miRNA expression across pathway classification and similarity-based MoA retrieval. HubmiRNet infers 414 pan-cancer hub miRNAs (HubmiRs) from 977 L1000 landmark genes, achieving a Pearson correlation coefficient of 87.72\\%; its 1,298-output variant also outperformed SiCmiR on the full-miRNA task (71.21\\% versus 67.30\\%). In the evaluated comparisons, miRNA augmentation provided more consistent gains than TF activity. Generic embedding controls showed model-dependent utility, while complementarity analyses identified a distinct, partially linearly recoverable representation that retained gene-derived structure. Illustrative rescue cases linked improved classification to biologically plausible miRNA patterns in samples with weak transcriptional signatures. These findings support inferred HubmiRs as a biologically informed recoding of transcriptomic data for perturbational drug modeling, while leaving recovery of measured perturbational miRNA responses to further validation.",
  "abstract_shown": true,
  "affiliations": [
    "School of Medicine, The Chinese University of Hong Kong, Shenzhen, Longgang District, Shenzhen, Guangdong 518172, China; Warshel Institute for Computational Bio"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 14,
  "authors": [
    {
      "name": "X. Cao"
    },
    {
      "name": "Y. Chen"
    },
    {
      "name": "J. Xu"
    },
    {
      "name": "Z. Zhang"
    },
    {
      "name": "X. Cheng"
    },
    {
      "name": "S. Wang"
    },
    {
      "name": "Y. Zhang"
    },
    {
      "name": "X. Cai"
    },
    {
      "name": "S. Cui"
    },
    {
      "name": "Z. Zhu"
    },
    {
      "name": "X. Ji"
    },
    {
      "affs": [
        0
      ],
      "name": "H.-Y. Huang"
    },
    {
      "name": "Y.-C.-D. Lin"
    },
    {
      "affs": [
        0
      ],
      "name": "H.-D. Huang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/cjkxp98qj13m4.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.17.752312",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.09.17.752312v1.full",
  "id": "cjkxp98qj13m4",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.09.17.752312v1",
  "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",
      "1703|Computational Drug Discovery Methods"
    ]
  ],
  "openalex": "W7213910805",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.09.17.752312v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-17",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/cjkxp98qj13m4.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:24:09Z",
      "native_id": "10.64898/2026.09.17.752312",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.09.17.752312v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "MIRCID: Inferred Hub-miRNAs Drive Cross-Task Improvements in Drug Mechanistic Modeling",
  "title_text": "MIRCID: Inferred Hub-miRNAs Drive Cross-Task Improvements in Drug Mechanistic Modeling",
  "type": "preprint",
  "updated_at": "2026-10-30T12:24:09Z",
  "url": "https://research.draconitic.com/paper/cjkxp98qj13m4",
  "venue": "bioRxiv",
  "versions": []
}