SMILE: systems metabolomics using interpretable learning and evolution

SMILE: systems metabolomics using interpretable learning and evolution

Abstract 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...

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Bibliographic Details
Main Authors: Chengyuan Sha, Miroslava Cuperlovic-Culf, Ting Hu
Format: Article
Language:English
Published: BMC 2021-05-01
Series:BMC Bioinformatics
Subjects:
Metabolomics
Alzheimer’s disease
Interpretable machine learning
Feature interaction
Evolutionary algorithm
Online Access:https://doi.org/10.1186/s12859-021-04209-1

Internet

https://doi.org/10.1186/s12859-021-04209-1

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