ArticleIEEE life sciences letters2016
Regularization Techniques to Overcome Overparameterization of Complex Biochemical Reaction Networks.
Article in IEEE life sciences letters, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Development of a hybrid model for a partially known intracellular signaling pathway through correction term estimation and neural network modeling.PLoS computational biology · 2020Article
- MODELING INTER-KINGDOM REGULATION OF INFLAMMATORY SIGNALING IN HUMAN INTESTINAL EPITHELIAL CELLS.Computers & chemical engineering · 2020Article
- Systems NMR: single-sample quantification of RNA, proteins and metabolites for biomolecular network analysis.Nature methods · 2019Article
- Modeling the Dynamics of Human Liver Failure Post Liver Resection.Processes (Basel, Switzerland) · 2018Article
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2 authors.
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Abstract
Models of biochemical reaction networks commonly contain a large number of parameters while at the same time there is only a limited amount of (noisy) data available for their estimation. As such, the values of many parameters are not well known as nominal parameter values have to be determined from the open scientific literature and a significant number of the values may have been derived in different cell types or organisms than that which is modeled. There clearly is a need to estimate at least some of the parameter values from experimental data, however, the small amount of available data and the large number of parameters commonly found in these types of models, require the use of regularization techniques to avoid over fitting. A tutorial of regularization techniques, including parameter set selection, precedes a case study of estimating parameters in a signal transduction network. Cross validation rather than fitting results are presented to further emphasize the need for models that generalize well to new data instead of simply fitting the current data.
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