SimMapNet: a Bayesian framework for gene regulatory network inference using gene ontology similarities as external hint
Maryam Shahdoust, Rosa Aghdam, Mehdi Sadeghi · 2026
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Description
Maryam Shahdoust, Rosa Aghdam, and Mehdi Sadeghi present SimMapNet, a method for inferring which genes regulate which others. A simple hunch guides it. Genes with similar documented functions are likelier to be wired together. Their 2026 Bayesian framework folds Gene Ontology similarity directly into the statistical prior of a network model.
Tested on a bacterial DNA-repair pathway and on fruit fly data, SimMapNet reconstructed networks more accurately than three established methods on the standard F1 score. It also runs fast enough for large networks. The clearest advantage appeared when samples were few, the setting where purely data-driven inference usually starves. Simulations backed that strength. The authors offer the tool as a principled way to let accumulated biological knowledge steady network building when experiments are small.
Catalog record
| CREATOR | Maryam Shahdoust, Rosa Aghdam, Mehdi Sadeghi |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 51060 |
| LICENSE | https://creativecommons.org/licenses/by/4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |