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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Bibliographic Details
Published in:	Machine learning Vol. 114; no. 11; p. 254
Main Authors:	Ai, Lun, Muggleton, Stephen H., Liang, Shi-Shun, Baldwin, Geoff S.
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.11.2025 Springer Nature B.V
Subjects:	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
Online Access:	Get full text
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