Public Status · Version 2026.08

Independent concept site — eligibility not established

This website does not represent an accredited university, nonprofit research institute, verified principal investigator, or Anthropic partner. Profiles, grants, events, facilities, and affiliations are illustrative unless independently verified.

Read the full status notice
BBQ Independent Research InitiativeIndependent research initiative · not an accredited institution · Warsaw, Poland
Operating Form
Online initiative
Domain
bbq.edu.pl
Eligibility
Not established
ILLUSTRATIVE PROJECT CONCEPT

Project BIO-EMBED: Multi-Modal Foundation Embeddings for Single-Cell Spatial Omics

Developing geometric deep learning architectures and foundation graph transformers to map cellular microenvironments and predict pharmacological perturbations in spatial transcriptomics.

Principal Investigator
Dr Mateusz Wójcik
Laboratory
Computational Biology & Medical Data Intelligence Group (CBMD Group)
Funding Agency
National Science Centre (NCN) — SONATA 19 Scheme
Grant ID
2023/51/D/NZ2/02844
Allocated Budget
PLN 1,780,000 (€400,000)
Project Period
2024–2027

Scientific Objective & Core Research Questions

How can spatial multi-omics and gene expression profiles be unified into a single geometric latent space to predict cellular drug responses in heterogeneous tumors?

Work Packages & Methodological Roadmap

  1. Constructing spatial cellular neighborhood graphs from multiplexed in situ sequencing data.
  2. Training self-supervised graph transformers on atlas-scale single-cell datasets (over 20 million cells).
  3. Validating cellular perturbation predictions in co-culture models with Warsaw Medical University.

Project Deliverables & Software Artefacts

  • BioEmbed-Transformer: Pre-trained foundation model for single-cell spatial transcriptomics.
  • Open spatial omics benchmark suite and clinical visualization portal.
  • ELIXIR-compliant FAIR data deposition package.

Project Description

Single-cell spatial biology provides unprecedented insight into the spatial organization of tissues and tumor microenvironments. Project BIO-EMBED creates foundational artificial intelligence models that process spatial transcriptomics and proteomics at single-cell resolution, unlocking new predictive tools for personalized oncology.

Project Milestones & Reporting

  • Ethics approval granted by Warsaw Medical University Bioethics Committee (KB/142/2024).
  • Presented as an invited talk at ISMB/ECCB 2026.

Foundation References & Publications

External scholarly reference · 2016Third-party work

The FAIR Guiding Principles for Scientific Data Management and Stewardship

Mark D. Wilkinson, Michel Dumontier, IJsbrand Jan Aalbersberg, Gabrielle Appleton, Myles Axton, Arie Baak, Niklas Blomberg, Jan-Willem Boiten, Luiz Bonino da Silva Santos, Philip E. Bourne, Jildau Bouwman, Anthony J. Brookes, Tim Clark, Mercè Crosas, Ingrid Dillo, Olivier Dumon, Scott Edmunds, Chris T. Evelo, Richard Finkers, Alejandra Gonzalez-Beltran, Alasdair J. G. Gray, Paul Groth, Carole Goble, Jeffrey S. Grethe, Jaap Heringa, Peter A. C. 't Hoen, Rob Hooft, Tobias Kuhn, Ruben Kok, Joost Kok, Scott J. Lusher, Maryann E. Martone, Albert Mons, Abel L. Packer, Bengt Persson, Philippe Rocca-Serra, Marco Roos, Rene van Schaik, Susanna-Assunta Sansone, Erik Schultes, Thierry Sengstag, Ted Slater, George Strawn, Morris A. Swertz, Mark Thompson, Johan van der Lei, Erik van Mulligen, Jan Velterop, Andra Waagmeester, Peter Wittenburg, Katherine Wolstencroft, Jun Zhao, Barend Mons

Scientific Data Vol. 3, pp. 160018(2016). DOI: 10.1038/sdata.2016.18

Defines the principles that digital research objects should be Findable, Accessible, Interoperable, and Reusable for people and machines.

External scholarly reference · 2021Third-party work

Datasheets for Datasets

Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna Wallach, Hal Daumé III, Kate Crawford

Communications of the ACM Vol. 64(12), pp. 86-92(2021). DOI: 10.1145/3458723

Introduces a documentation framework for recording the motivation, composition, collection process, recommended uses, and limitations of datasets used in machine learning.