Identification of the Structure of a Probabilistic Boolean Network from Samples Including Frequencies of Outcomes

Tatsuya Akutsu, Avraham A. Melkman

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

We study the problem of identifying the structure of a probabilistic Boolean network (PBN), a probabilistic model of biological networks, from a given set of samples. This problem can be regarded as an identification of a set of Boolean functions from samples. Existing studies on the identification of the structure of a PBN only use information on the occurrences of samples. In this paper, we also make use of the frequencies of occurrences of subtuples, information that is obtainable from the samples. We show that under this model, it is possible to identify a PBN from among a class of PBNs, for much broader classes of PBNs. In particular, we prove that, under a reasonable assumption, the structure of a PBN can be identified from among the class of PBNs that have at most three functions assigned to each node, but that identification may be impossible if four or more functions are assigned to each node. We also analyze the sample complexity for exactly identifying the structure of a PBN, and present an efficient algorithm for the identification of a PBN consisting of threshold functions from samples.

Original languageEnglish
Article number8587124
Pages (from-to)2383-2396
Number of pages14
JournalIEEE Transactions on Neural Networks and Learning Systems
Volume30
Issue number8
DOIs
StatePublished - 1 Aug 2019

Keywords

  • Boolean functions
  • probabilistic Boolean networks (PBNs)
  • sample complexity
  • threshold functions

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence

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