iSCHRUNK specifications

Information


Unique identifier OMICS_32146
Name iSCHRUNK
Alternative name in Silico approach to CHaracterization and Reduction of UNcertainty in the Kinetic models

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Publication for in Silico approach to CHaracterization and Reduction of UNcertainty in the Kinetic models

iSCHRUNK citation

library_books

Machine Learning Methods for Analysis of Metabolic Data and Metabolic Pathway Modeling

2018
PMCID: 5875994
PMID: 29324649
DOI: 10.3390/metabo8010004

[…] c parameters for genome-scale level models with kinetic consideration, Andreozzi et al. [] have utilized machine learning methods in combination with kinetic modeling principles in the approach named iSCHRUNK. In this approach, machine learning uses values of observed data samples to infer parameter ranges that predict whether data can satisfy a given property. The goal of the method is to determi […]

iSCHRUNK institution(s)
Laboratory of Computational Systems Biotechnology (LCSB), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
iSCHRUNK funding source(s)
Supported by funding from the Ecole Polytechnique Fédérale de Lausanne (EPFL), the 2015/313 ERASysAPP RobustYeast Project funded through SystemsX.ch, the Swiss Initiative for Systems Biology evaluated by the Swiss National Science Foundation, and the Swiss National Science Foundation grant 315230_163423.

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