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Construction Method of Probabilistic Boolean Networks Based on Imperfect Information

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Title: Construction Method of Probabilistic Boolean Networks Based on Imperfect Information
Authors: Umiji, Katsuaki Browse this author
Kobayashi, Koichi Browse this author →KAKEN DB
Yamashita, Yuh Browse this author →KAKEN DB
Keywords: gene regulatory network
linear programming
matrix-based representation
probabilistic Boolean network
Issue Date: Dec-2019
Publisher: MDPI
Journal Title: Algorithms
Volume: 12
Issue: 12
Start Page: 268
Publisher DOI: 10.3390/a12120268
Abstract: A probabilistic Boolean network (PBN) is well known as one of the mathematical models of gene regulatory networks. In a Boolean network, expression of a gene is approximated by a binary value, and its time evolution is expressed by Boolean functions. In a PBN, a Boolean function is probabilistically chosen from candidates of Boolean functions. One of the authors has proposed a method to construct a PBN from imperfect information. However, there is a weakness that the number of candidates of Boolean functions may be redundant. In this paper, this construction method is improved to efficiently utilize given information. To derive Boolean functions and those selection probabilities, the linear programming problem is solved. Here, we introduce the objective function to reduce the number of candidates. The proposed method is demonstrated by a numerical example.
Rights: https://creativecommons.org/licenses/by/4.0/
Type: article
URI: http://hdl.handle.net/2115/76727
Appears in Collections:情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

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