title: Comparative study of computational methods for reconstructing genetic networks of cancer-related pathways.
abstract: Network reconstruction is an important yet challenging task in systems biology. While many methods have been recently proposed for reconstructing biological networks from diverse data types, properties of estimated networks and differences between reconstruction methods are not well understood. In this paper, we conduct a comprehensive empirical evaluation of seven existing network reconstruction methods, by comparing the estimated networks with different sparsity levels for both normal and tumor samples. The results suggest substantial heterogeneity in networks reconstructed using different reconstruction methods. Our findings also provide evidence for significant differences between networks of normal and tumor samples, even after accounting for the considerable variability in structures of networks estimated using different reconstruction methods. These differences can offer new insight into changes in mechanisms of genetic interaction associated with cancer initiation and progression.
authors.affiliation: Computer Engineering Department, Iran University of Science and Technology, Tehran, Iran.
authors.forename: Nafiseh
authors.initials: N
authors.lastname: Sedaghat
authors.affiliation: Department of Biostatistics, University of Washington, Seattle, WA, USA.
authors.forename: Takumi
authors.initials: T
authors.lastname: Saegusa
authors.affiliation: Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
authors.forename: Timothy
authors.initials: T
authors.lastname: Randolph
authors.affiliation: Department of Biostatistics, University of Washington, Seattle, WA, USA.
authors.forename: Ali
authors.initials: A
authors.lastname: Shojaie
keywords: differential network analysis; genetic networks; graphical models; network reconstruction