TSProm: deep learning framework to predict tissue-specific regulatory logic
Pallavi Surana, Pratik Dutta, Nimisha Papineni, Rekha Sathian, Zhihan Zhou, Han Liu, Ramana V Davuluri · 2026
Read TSProm: deep learning framework to predict tissue-specific regulatory logic by Pallavi Surana, Pratik Dutta, Nimisha Papineni, Rekha Sathian, Zhihan Zhou, Han Liu, Ramana V Davuluri (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-13233143.txt.
Description
Pallavi Surana and colleagues published a 2026 computational study introducing TSProm, a deep learning system that reads the DNA switches controlling tissue-specific genes. They act differently in brain, liver, or testis partly because of promoter sequences near their start. The team adapted a DNA foundation model to separate general promoter rules from the signals that mark one tissue.
Two specialized models were trained, one for promoter biology overall and one for tissue-specific control, then interpretation tools highlighted the sequence motifs each model used. Applied to human brain, liver, and testis promoters, the system flagged transcription factors such as SP1, MYC, and HES6 in brain tissue, factors also tied to brain tumors. A family of zinc finger proteins stood out across tissues.
Catalog record
| CREATOR | Pallavi Surana, Pratik Dutta, Nimisha Papineni, Rekha Sathian, Zhihan Zhou, Han Liu, Ramana V Davuluri |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 45 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |