BioParity.UFO / UAP research

2 sources for “Computational biology”

Results

PaperS7678

Annotation-free discovery of disease-relevant cells in single-cell datasets

Erin Craig, Timothy J Keyes, Jolanda Sarno, Jeremy P D’Silva, Pablo Domizi, Maxim Zaslavsky, Albert Tsai, David Glass, Garry P Nolan, Trevor Hastie, Robert Tibshirani, Kara L Davis. ebi.ac.uk. August 27, 2025.

This Nolan-coauthored computational study estimates disease-associated cells using patient disease labels and a healthy baseline, without requiring expert cell annotations. Reported leukemia examples support a research method, with stronger expert-label benchmarks, small patient cohorts and assumptions that limit interpretation. Millions of measured cells do not imply millions of independent patients. Disclosed NIH support does not establish a classified programme or UAP access.

Complete supplied XML text; graphics, algorithms and supplements unreviewed
PaperS7679

Integration of spatial and single-cell data across modalities with weakly linked features

Shuxiao Chen, Bokai Zhu, Sijia Huang, John W Hickey, Kevin Z Lin, Michael Snyder, William J Greenleaf, Garry P Nolan, Nancy R Zhang, Zongming Ma. ebi.ac.uk. September 7, 2023.

This Nolan-coauthored study introduces MaxFuse, a computational method for aligning cell measurements across weakly linked data types. Benchmarks report gains on selected datasets; inferred spatial gene activity is distinguished from measurements actually made in each cell. Reused cell batches and tuned comparisons limit broader conclusions. The article explicitly attributes FDA Medical Countermeasures Initiative contracts and NIH support to Nolan, without reporting a secret approach or classified UAP programme.

Complete supplied XML text; original figures and supplements unreviewed

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