Supervised, semi-supervised and unsupervised inference of gene regulatory networks

Mapping the topology of gene regulatory networks is a central problem in systems biology. The regulatory architecture controlling gene expression also controls consequent cellular behavior such as development, differentiation, homeostasis and response to stimuli, while deregulation of these networks has been implicated in oncogenesis and tumor progression (Pe'er and Hacohen, 2011). Experimental methods based e.g. on chromatin immunoprecepitation, DNaseI hypersensitivity or protein-binding assays are capable of determining the nature of gene regulation in a given system, but are time-consuming, expensive and require antibodies for each transcription factor (Elnitski et al., 2006). Accurate computational methods to infer gene regulatory networks, particularly methods that leverage genome-scale experimental data, are urgently required not only to supplement empirical approaches but also, if possible, to explore these data in new, moreintegrative ways. Many computational methods have been developed to infer regulatory networks from gene expression data, predominately employing unsupervised techniques. Several comparisons have been made of network inference methods, but a comprehensive evaluation that covers unsupervised, semi-supervised and supervised methods is lacking, and many questions remain open.