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Showing 1–4 of 4 results
Advanced filters: Author: Alejandro Tejada-Lapuerta Clear advanced filters

  • Pretrained on SpatialCorpus-110M, a curated resource of vast and diverse transcriptomes of dissociated and spatially resolved cells from both human and mouse, Nicheformer advances toward building foundation models for spatial single-cell analysis.

    • Alejandro Tejada-Lapuerta
    • Anna C. Schaar
    • Fabian J. Theis
    ResearchOpen Access
    Nature Methods
    Volume: 22, P: 2525-2538

  • This Perspective explores causal machine learning in single-cell genomics, addressing challenges such as generalization, interpretability and cell dynamics, while highlighting advances and the potential to uncover new insights into cellular mechanisms.

    • Alejandro Tejada-Lapuerta
    • Paul Bertin
    • Fabian J. Theis
    Reviews
    Nature Genetics
    Volume: 57, P: 797-808

  • The development of multimodal foundation models, pretrained on diverse omics datasets, to unravel the intricate complexities of molecular cell biology is envisioned.

    • Haotian Cui
    • Alejandro Tejada-Lapuerta
    • Bo Wang
    Reviews
    Nature
    Volume: 640, P: 623-633

  • This Perspective presents a comprehensive and in-depth overview of computational models based on the deep learning architecture of transformers for single-cell omics analysis.

    • Artur Szałata
    • Karin Hrovatin
    • Fabian J. Theis
    Reviews
    Nature Methods
    Volume: 21, P: 1430-1443