Biological interaction networks based on sparse temporal expansion of graphical models

Biological networks are often described as probabilistic graphs in the context of gene and protein sequence analysis in molecular biology. Microarrays and proteomics technology allow the monitoring of expression levels over thousands of biological units over time. In experimental efforts we are interested in unveiling pairwise interactions. Many graphical models have been introduced in order to discover associations from the expression data analysis. However, the small size of samples compared to the number of observed genes/proteins makes the inference of the network structure quite challenging. In this study we generate gene-protein networks from sparse experimental data using two methods, partial correlations and Kernel Density Estimation, in order to capture genetic interactions. Dynamic Gaussian analysis is used to match special characteristics to genes and proteins at different time stages utilizing the KDE method for expressing Gaussian associations with non-linear parameters.