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...

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Detalles Bibliográficos
Autores principales: Alazmi, Meshari, Kuwahara, Hiroyuki, Soufan, Othman, Ding, Lizhong, Gao, Xin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2019
Materias:
Original Papers
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

Internet

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

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