Biological interaction networks based on non-parametric estimation

Abstract

Biological networks are often described as probabilistic graphs in the context of gene and protein sequence analysis in molecular biology. Microarrays and proteomics technologies facilitate the monitoring of expression levels over thousands of biological units over time. Several experimental efforts have appeared aiming to unveiling pairwise interactions, with many graphical models being introduced in order to discover associations from 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 temporal data using two methods, partial correlations and Kernel Density Estimation (KDE), in an attempt to capture genetic interactions. Applying KDE method we model the genetic associations as Gaussians approximations, while through the dynamic Gaussian analysis we aim to identify relationships between genes and proteins at different time stages. The statistical results demonstrate valid biological interactions and indicate potential new indirect relations that deserve further biological examination for validation.