**Pipeline4DGEData**

These functions implement the Pipeline4DGEData, which consists of a series of statistical modeling techniques to construct dynamic gene regulatory networks from the large volumes of high-dimensional time-course gene expression data that are freely available in the Gene Expression Omnibus repository.

This pipeline has a consistent and scalable structure that allows it to simultaneously analyze a large number of time-course gene expression data sets, and then integrate the results across different studies.

**GenPen**

Ordinary differential equations (ODEs) describe the dynamics of a continuously changing process by relating the process to its rates of change. Generalized smoothing aims to obtain an estimated functional entity that adheres to the data and incorporates domain-specific information defined by the ODE. This approach facilitates the expression of the parameters of the ODE explicitly in terms of the parameters defining the functional entity. Thus providing an efficient and easy-to-use estimation procedure that obtains estimates that have comparable accuracy to those produced by parameter cascading with proling approach.

**Data2LD in Matlab; ****Data2LD in R**

Data2LD, is derived from the collocation inference approach of Ramsay et al. (2007), but takes advantage of the linearity of the system to achieve fast and stable computation. Comparisons with older two-stage and nonlinear least squares methods reveal greatly improved bias and sampling variance.

Dynamic smoothing is defined as estimation of forcing functions impacting

simple linear buffers, and permits data smoothing in the form of an estimated input function passed through a linear buffer.

**IHC**

Many pragmatic clustering methods have been developed to group data vectors or objects into clusters so that the objects in one cluster are very similar and objects in different clusters are distinct based on some similarity measure. However, there is still a need for the development of time-course clustering methods that can adequately deal with inhomogeneous clusters (some clusters are quite large and others are quite small). Here we propose two such methods, hierarchical clustering (IHC) and iterative pairwise-correlation clustering (IPC).