title: Reverse engineering gene regulatory networks from measurement with missing values.
abstract: BACKGROUND
  Gene expression time series data are usually in the form of high-dimensional arrays. Unfortunately, the data may sometimes contain missing values: for either the expression values of some genes at some time points or the entire expression values of a single time point or some sets of consecutive time points. This significantly affects the performance of many algorithms for gene expression analysis that take as an input, the complete matrix of gene expression measurement. For instance, previous works have shown that gene regulatory interactions can be estimated from the complete matrix of gene expression measurement. Yet, till date, few algorithms have been proposed for the inference of gene regulatory network from gene expression data with missing values.
  
  
  RESULTS
  We describe a nonlinear dynamic stochastic model for the evolution of gene expression. The model captures the structural, dynamical, and the nonlinear natures of the underlying biomolecular systems. We present point-based Gaussian approximation (PBGA) filters for joint state and parameter estimation of the system withone-steportwo-step missing measurements. The PBGA filters use Gaussian approximation and various quadrature rules, such as the unscented transform (UT), the third-degree cubature rule and the central difference rule for computing the related posteriors. The proposed algorithm is evaluated with satisfying results for synthetic networks, in silico networks released as a part of the DREAM project, and the real biological network, the in vivo reverse engineering and modeling assessment (IRMA) network of yeastSaccharomyces cerevisiae.
  
  
  CONCLUSIONS
  PBGA filters are proposed to elucidate the underlying gene regulatory network (GRN) from time series gene expression data that contain missing values. In our state-space model, we proposed a measurement model that incorporates the effect of the missing data points into the sequential algorithm. This approach produces a better inference of the model parameters and hence, more accurate prediction of the underlying GRN compared to when using the conventional Gaussian approximation (GA) filters ignoring the missing data points.
authors.affiliation: Department of Electrical Engineering, Columbia University, 500 W 120th Street, New York, 10027 NY USA.
authors.forename: Oyetunji E
authors.initials: OE
authors.lastname: Ogundijo
authors.affiliation: Department of Electrical Engineering, Columbia University, 500 W 120th Street, New York, 10027 NY USA.
authors.forename: Abdulkadir
authors.initials: A
authors.lastname: Elmas
authors.affiliation: Department of Electrical Engineering, Columbia University, 500 W 120th Street, New York, 10027 NY USA.
authors.forename: Xiaodong
authors.initials: X
authors.lastname: Wang
keywords: Bayesian inference; Gaussian filters; Gene expression; Missing data; Network inference