A New Method for Modeling Complex Biological Systems
Author Information
Author(s): Tøndel Kristin, Indahl Ulf G, Gjuvsland Arne B, Vik Jon Olav, Hunter Peter, Omholt Stig W, Martens Harald
Primary Institution: Norwegian University of Life Sciences
Hypothesis
A more accurate mapping can be obtained by locally linear or locally polynomial regression.
Conclusion
HC-PLSR is a promising approach for metamodelling in systems biology, especially for highly nonlinear or non-monotone parameter to phenotype maps.
Supporting Evidence
- HC-PLSR outperformed both polynomial PLSR and OLS regression in all test cases.
- The advantage of HC-PLSR was largest in systems with highly nonlinear functional relationships.
- HC-PLSR can flexibly adjust to suit the complexity of dynamic model behavior.
Takeaway
This study shows a new way to model complex biological systems that helps predict how changes in parameters affect outcomes, especially when those relationships are complicated.
Methodology
The study used a new method called HC-PLSR, which involves clustering data and applying local regression models to improve predictions.
Limitations
The method may require a good initial global model for effective clustering and prediction.
Digital Object Identifier (DOI)
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