Presentation 2020-11-25
Data-Driven Optimal Reconstruction of Boolean Networks with Noisy Dynamics
Koichi Kobayashi, Yuhu Wu,
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Abstract(in Japanese) (See Japanese page)
Abstract(in English) A Boolean network is a mathematical model that the dynamics are represented by Boolean functions. In this paper, a method for reconstructing a Boolean network with noisy dynamics from data is proposed. We show that the noisy dynamics maximizing a selection probability of a given Boolean function are derived as a constant.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) Boolean network / noisy dynamics / optimal reconstruction / gene regulatory network / systems biology
Paper # CAS2020-25,MSS2020-17
Date of Issue 2020-11-18 (CAS, MSS)

Conference Information
Committee MSS / CAS / IPSJ-AL
Conference Date 2020/11/25(2days)
Place (in Japanese) (See Japanese page)
Place (in English) Online
Topics (in Japanese) (See Japanese page)
Topics (in English)
Chair Shigemasa Takai(Osaka Univ.) / Yasuhiro Takashima(Univ. of Kitakyushu)
Vice Chair Atsuo Ozaki(Osaka Inst. of Tech.) / Hiroki Sato(Sony LSI Design)
Secretary Atsuo Ozaki(Setsunan Univ.) / Hiroki Sato(Hokkaido Univ.) / (Yamanashi Univ.)
Assistant Naoki Hayashi(Osaka Univ.) / Motoi Yamaguchi(TECHNOPRO) / Yohei Nakamura(Hitachi)

Paper Information
Registration To Technical Committee on Mathematical Systems Science and its applications / Technical Committee on Circuits and Systems / Special Interest Group on Algorithms
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Data-Driven Optimal Reconstruction of Boolean Networks with Noisy Dynamics
Sub Title (in English)
Keyword(1) Boolean network
Keyword(2) noisy dynamics
Keyword(3) optimal reconstruction
Keyword(4) gene regulatory network
Keyword(5) systems biology
1st Author's Name Koichi Kobayashi
1st Author's Affiliation Hokkaido University(Hokkaido Univ.)
2nd Author's Name Yuhu Wu
2nd Author's Affiliation Dalian University of Technology(Dalian Univ. of Tech.)
Date 2020-11-25
Paper # CAS2020-25,MSS2020-17
Volume (vol) vol.120
Number (no) CAS-245,MSS-246
Page pp.pp.34-39(CAS), pp.34-39(MSS),
#Pages 6
Date of Issue 2020-11-18 (CAS, MSS)