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Systematic selection of chemical fingerprint features improves the Gibbs energy prediction of biochemical reactions
MOTIVATION: Accurate and wide-ranging prediction of thermodynamic parameters for biochemical reactions can facilitate deeper insights into the workings and the design of metabolic systems. RESULTS: Here, we introduce a machine learning method with chemical fingerprint-based features for the predicti...
| Autores principales: | , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Oxford University Press
2019
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6662295/ https://www.ncbi.nlm.nih.gov/pubmed/30590445 http://dx.doi.org/10.1093/bioinformatics/bty1035 |