Statistical and Machine Learning Approaches for Network Analysis

5.1 Introduction

The inference of gene regulatory networks aims to unveil the causal structure of the relations among genes in a cellular system from gene expression data [1-4]. Gene regulatory networks (GRNs) allow to organize genes according to their gene expression dependency structure and aim to complement the understanding of the molecular structures and processes in complex organismal cellular systems. The vast amount of gene regulatory network inference methods that are being developed are gaining more and more popularity due to the astonishing increase of high-throughput dataset generation. The challenge of the future is the development of novel statistical methods to benefit from present and new emerging mass data [5,6], for example, from microarray, Chip–Chip [7], Chip–seq, proteomics mass spectrometry, protein arrays, and RNA–seq. Due to the large amount of available samples and cost efficiency, DNA microarrays are still the state-of-the-art data source for gene regulatory network inference. For example, one of the largest data repository of such high-throughput gene expression data is the GEO database [8] that provides a large range of observational [9,10] and experimental gene expression data. Such large-scale datasets for different organisms, perturbation and disease conditions, enable system-wide studies of species, and phenotype-specific gene regulatory networks.

The edges in gene regulatory networks represent physical interactions between genes, intermediates, ...