{
  "abstract": "Longitudinal omics datasets combine high-dimensional repeated measurements with missing observations, complicating supervised modelling and temporal interpretation. We present TensorPLS, an R package integrating missing-data handling, matrix-based partial least squares discriminant analysis (PLS-DA), structured variable importance, subject-level nested cross-validation and downstream functional enrichment. Data are represented as subjects x features x time tensors. Missing values are handled by Tucker3 reconstruction or feature-time mean imputation, after which tensors are unfolded along the subject mode for PLS-DA. Cumulative variable importance in projection (VIP) scores are mapped to feature-time coordinates and summarised across time and cumulative PLS models. During nested cross-validation, imputation, component tuning, VIP-based feature selection and refitting are performed within training partitions. Full-cohort models are fitted separately for descriptive interpretation. We evaluated TensorPLS on two public longitudinal transcriptomic datasets using five-by-five nested cross-validation. In GSE68310 (109 subjects), Tucker3 reconstruction yielded Q2Y = 0.670, AUROC 0.971 and balanced accuracy 0.931. In GSE182616 (66 subjects), feature-time mean imputation yielded Q2Y = 0.294, AUROC 0.827 and balanced accuracy 0.756. Predictive metrics were calculated from pooled outer held-out predictions. TensorPLS supports predictive assessment, functional enrichment of selected genes and temporal interpretation of model-assigned feature importance.",
  "abstract_shown": true,
  "affiliations": [
    "Telethon Institute Of Genetics And Medicine: Naples, Campania, IT"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "A. Giordano"
    },
    {
      "name": "A. Conesa"
    },
    {
      "name": "L. Balzano Nogueira"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1n7tr99rjxw3g.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.11.03.763745",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.11.03.763745v1.full",
  "id": "1n7tr99rjxw3g",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.11.03.763745v1",
  "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": null,
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.11.03.763745v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-03",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/1n7tr99rjxw3g.ris",
  "sources": [
    {
      "first_seen": "2026-11-05T00:21:57Z",
      "native_id": "10.64898/2026.11.03.763745",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.11.03.763745v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "TensorPLS: an R package for exploring molecular and temporal patterns in longitudinal omics data using PLS-DA",
  "title_text": "TensorPLS: an R package for exploring molecular and temporal patterns in longitudinal omics data using PLS-DA",
  "type": "preprint",
  "updated_at": "2026-11-06T00:21:41Z",
  "url": "https://research.draconitic.com/paper/1n7tr99rjxw3g",
  "venue": "bioRxiv",
  "versions": []
}