ArticleiScience2026
Causal AI digital twin for bioprocess bottleneck diagnosis via metabolic flexibility and rigidification maps.
Article in iScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
3 authors.
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Abstract
Diagnosing underperforming non-model microbial cultures remains difficult. To address this, we developed an intervention-aware genome-informed digital twin with interpretable flexibility features, explainable learning, and causal-structure discovery to convert sparse anchors into actionable diagnostic artifacts. We grew
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.