Bayesian inference of gene regulatory networks at stochastic steady state
Anshi Gupta, Ryeongkyung Yoon, Kresimir Josic · 2026
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Description
Gupta, Yoon, and Josic inferred gene regulatory networks from steady-state cell data with Bayesian statistics in twenty twenty-six. Such networks decide which genes are on or off. Their wiring limits how signals flow through a cell. Most methods for inferring them skip the basic chemistry behind noisy gene activity.
Their method models gene activity with the chemical Langevin equation at a random steady state. So no time-course data are needed. A regularized horseshoe prior, a tool that shrinks weak links toward zero, fits the known sparseness of real networks. Strong regulatory edges still stand out. On synthetic data with known truth, the method found the network's structure and its kinetic parameters. It even found regulatory cycles. The authors argue the Bayesian path reads clearly in biology terms. It pins down structure in a way plain statistics or deep learning methods do not.
Catalog record
| CREATOR | Anshi Gupta, Ryeongkyung Yoon, Kresimir Josic |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 68 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |