Boolean matrix logic programming for active learning of gene functions in genome-scale metabolic network models

Reasoning about hypotheses and updating knowledge through empirical observations are central to scientific discovery. In this work, we applied logic-based machine learning methods to drive biological discovery by guiding experimentation. Genome-scale metabolic network models (GEMs) - comprehensive r...

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Vydané v:	Machine learning Ročník 114; číslo 11; s. 254
Hlavní autori:	Ai, Lun, Muggleton, Stephen H., Liang, Shi-Shun, Baldwin, Geoff S.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Springer US 01.11.2025 Springer Nature B.V
Predmet:	Annotations Artificial Intelligence Boolean Computer Science Control Design of experiments Design optimization E coli Experimentation Experiments Genes Genetic engineering Genomes Hypotheses Logic programming Logic programs Machine Learning Mechatronics Metabolism Metabolites Microorganisms Natural Language Processing (NLP) Robotics Robots Simulation and Modeling Synthetic biology Matrix Inductive logic programming Active learning Computational scientific discovery
ISSN:	0885-6125, 1573-0565
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