DAG-VAERL: a novel causal inference method for building causal gene regulatory networks
Teng Long, Sachit Satyal, Yong-Fang Kuo, Jean Gao · 2026
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Description
Teng Long and colleagues propose in 2026 a new computer method for inferring cause-and-effect links between long non-coding RNAs and their target genes. Learning such networks as directed graphs without cycles is hard when links are curved or data are discrete.
Their system blends neural networks, attention weighting and reinforcement learning inside a generative model that proposes graph structures. Tests on artificial data and on Alzheimer disease data showed better recovery of true structures than older methods, especially with complex patterns. The authors position the approach as a general tool for causal discovery, with gene regulation as the driving application.
Catalog record
| CREATOR | Teng Long, Sachit Satyal, Yong-Fang Kuo, Jean Gao |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 116731 |
| LICENSE | https://creativecommons.org/licenses/by/4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |