FLASH-P: AI multi-agent framework builds accurate causal biological networks from literature in under an hour
By
Christos MitsanisNicole FortunaChristine BeveridgeDavid Kainer
Summary
FLASH-P is a multi-agent AI framework that autonomously curates scientific literature to construct causal, signed-directed network models for any trait-species combination in under an hour. It achieved 90% mean accuracy predicting outcomes of 1,088 published perturbations across seven species. The framework outperforms knowledge-graph derived networks due to its regulatory topology, and its merging agent can combine multiple networks while preserving accuracy and recovering pleiotropic effects.
Source
bskyFLASH-P: AI multi-agent framework builds accurate causal biological networks from literature in under an hoursciety.orgKey quotes
· 5 pulledFLASH-P, a multi-agent framework that autonomously curates this literature into perturbable, signed-directed network models for any trait–species combination in under an hour without much computational power.
Twelve FLASH-P networks across seven species predicted the directional outcome of 1,088 published perturbations with a mean accuracy of 90%.
This accuracy was driven by the regulatory topology FLASH-P constructs, which is why it outperformed knowledge-graph derived networks.
Its merging agent combined six networks into one that preserved single-trait accuracy and recovered pleiotropic effects, and consolidated independent runs of one trait into a comprehensive, high-accuracy network.
Mechanistic networks that encode causal regulatory logic can predict the effects of genetic and environmental perturbations but constructing them is a bottleneck in systems biology.
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