Nonlinear bionetwork structure inference using the random sampling-high dimensional model representation (RS-HDMR) algorithm.
Annu Int Conf IEEE Eng Med Biol Soc
; 2009: 6412-5, 2009.
Article
in En
| MEDLINE
| ID: mdl-19964421
ABSTRACT
This work presents the random sampling - high dimensional model representation (RS-HDMR) algorithm for identifying complex bionetwork structures from multivariate data. RS-HDMR describes network interactions through a hierarchy of input-output (IO) functions of increasing dimensionality. Sensitivity analysis based on the calculated RS-HDMR component functions provides a statistically interpretable measure of network interaction strength, and can be used to efficiently infer network structure. Advantages of RS-HDMR include the ability to capture nonlinear and cooperative realtionships among network components, the ability to handle both continuous and discrete relationships, the ability to be used as a high-dimensional IO model for quantitative property prediction, and favorable scalability with respect to the number of variables. To demonstrate, RS-HDMR was applied to experimental data measuring the single-cell response of a protein-protein signaling network to various perturbations. The resultant analysis identified the network structure comparable to that reported in the literature and to the results from a previous Bayesian network (BN) analysis. The IO model also revealed several nonlinear feedback and cooperative mechanisms that were unidentified through BN analysis.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Biopolymers
/
Algorithms
/
Signal Transduction
/
Models, Biological
Type of study:
Clinical_trials
/
Prognostic_studies
/
Risk_factors_studies
Language:
En
Journal:
Annu Int Conf IEEE Eng Med Biol Soc
Year:
2009
Document type:
Article
Affiliation country: