title: Statistical inference and reverse engineering of gene regulatory networks from observational expression data.
abstract: In this paper, we present a systematic and conceptual overview of methods for inferring gene regulatory networks from observational gene expression data. Further, we discuss two classic approaches to infer causal structures and compare them with contemporary methods by providing a conceptual categorization thereof. We complement the above by surveying global and local evaluation measures for assessing the performance of inference algorithms.
authors.affiliation: Computational Biology and Machine Learning Lab, School of Medicine, Dentistry and Biomedical Sciences, Center for Cancer Research and Cell Biology, Queen's University Belfast Belfast, UK.
authors.forename: Frank
authors.initials: F
authors.lastname: Emmert-Streib
authors.forename: Galina V
authors.initials: GV
authors.lastname: Glazko
authors.forename: Gökmen
authors.initials: G
authors.lastname: Altay
authors.forename: Ricardo
authors.initials: R
authors.lastname: de Matos Simoes