title: Exact Identification of the Structure of a Probabilistic Boolean Network from Samples.
abstract: We study the number of samples required to uniquely determine the structure of a probabilistic Boolean network (PBN), where PBNs are probabilistic extensions of Boolean networks. We show via theoretical analysis and computational analysis that the structure of a PBN can be exactly identified with high probability from a relatively small number of samples for interesting classes of PBNs of bounded indegree. On the other hand, we also show that there exist classes of PBNs for which it is impossible to uniquely determine the structure of a PBN from samples.
authors.forename: Xiaoqing
authors.initials: X
authors.lastname: Cheng
authors.forename: Tomoya
authors.initials: T
authors.lastname: Mori
authors.forename: Yushan
authors.initials: Y
authors.lastname: Qiu
authors.forename: Wai-Ki
authors.initials: WK
authors.lastname: Ching
authors.forename: Tatsuya
authors.initials: T
authors.lastname: Akutsu
mesh_terms: D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D005786:Gene Expression Regulation / Q000502:physiology*; D008954:Models, Biological; D015233:Models, Statistical; D020543:Proteome / Q000378:metabolism*; D018401:Sample Size; D015398:Signal Transduction / Q000502:physiology*
chemical_list: D020543:Proteome