scHILL: deciphering individual-level immune cell heterogeneity with single-cell RNA sequencing data
Yi Wang, Hongyu Li, Lun Li, Yongrong Cao, Zhijian Duan, Shuhui Song · 2026
Read scHILL: deciphering individual-level immune cell heterogeneity with single-cell RNA sequencing data by Yi Wang, Hongyu Li, Lun Li, Yongrong Cao, Zhijian Duan, Shuhui Song (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-13273425.txt.
Description
Yi Wang and a team of co-authors present a deep learning frame for reading single-cell data person by person, in a paper dated twenty twenty-six. Most tools sort cells well but blur how immune mixes differ between people. Their scHILL pairs a masked autoencoder with a scoring network.
Pretraining without labels helps when samples are few. The scorer then rates each person's cells and genes for weight in a condition. Across infection, autoimmune, and cancer datasets it beat rival methods at phenotype prediction. The authors pitch it as a step toward care tuned to one patient's immune makeup.
Catalog record
| CREATOR | Yi Wang, Hongyu Li, Lun Li, Yongrong Cao, Zhijian Duan, Shuhui Song |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 43 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |