Hidden causal inference delineates dynamic lncRNA regulation in autism spectrum disorder
Junpeng Zhang, Xuemei Wei, Chunwen Zhao, Zidan Hu, Shujie Fan · 2026
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Description
Junpeng Zhang and colleagues built a causal mapping tool for autism research in 2026. Long non-coding RNAs help regulate genes in the brain. Their shifting roles in autism remain unmapped. Static causal methods miss change across brain contexts. The team's new framework infers hidden, dynamic cause links. They call the hidden pattern dark causality. It mixes positive and negative dependencies at once. Analysis spanned several biological settings in autism data.
Dark causality proved common in this RNA regulation. Patterns shifted across contexts rather than staying fixed. Eight autism-linked RNAs tied to disrupted gene programs. RNA signatures separated sex and cell type with high accuracy. Twenty immune-related RNAs joined predicted brain-immune control. Cell-to-cell talk networks differed between autism and control groups. The tool matched rival causal methods on benchmarks. It beat correlation mapping. The authors offer it as a framework for dynamic regulation studies.
Catalog record
| CREATOR | Junpeng Zhang, Xuemei Wei, Chunwen Zhao, Zidan Hu, Shujie Fan |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 62 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |