SMILE: systems metabolomics using interpretable learning and evolution

SMILE: systems metabolomics using interpretable learning and evolution

Background: Direct link between metabolism and cell and organism phenotype in health and disease makes metabolomics, a high throughput study of small molecular metabolites, an essential methodology for understanding and diagnosing disease development and progression. Machine learning methods have se...

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Bibliographic Details
Main Authors: Cuperlovic-Culf, M. (Author), Hu, T. (Author), Sha, C. (Author)
Format: Article
Language:English
Published: BioMed Central Ltd 2021
Subjects:
algorithm
Algorithms
Alzheimer’s disease
Association reactions
biology
Biomedical research
Biomolecules
Computational Biology
Computational framework
Decision making
Decision-making mechanisms
Diagnosis
Disease development
Evolutionary algorithm
Evolutionary algorithms
Feature interaction
Graphical user interfaces
High throughput studies
Interpretable machine learning
Learning and evolution
machine learning
Machine learning
Machine Learning
Machine learning methods
Machine learning models
Metabolism
Metabolites
metabolomics
Metabolomics
Predictive analytics
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