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Matsuno Hiroshi


Prediction of debacle parts for robustness in a cell by using recurrent neural networks

大島商船高等専門学校紀要 Volume 37 Page 1-7
published_at 2004-12
OS10037000001.pdf
[fulltext] 5.39 MB
Title
リカレントニューラルネットワークを用いた 細胞内反応システムにおけるロバストネス瓦解部位予測
Prediction of debacle parts for robustness in a cell by using recurrent neural networks
Creators Kitakaze Hironori
Creators Matsuno Hiroshi
Creators Ikeda Nobuhiko
Creators Miyano Satoru
Source Identifiers
Creator Keywords
Petri Net Genomic Object Net Recurrent Neural Networks BPTT
Living organisms have sophisticated control mecha,nism to keep biological system robust against abnormalities from inside/outside of them. However, at the same time, the control mechanism has a critical point at which the stability can be broken easily. This paper proposes a method to find critical points of the control mechanism in a biological pathway described by hybrid functional Petri nets (HFPN). In this method. HFPNs are converted to a recurrent neural networks (RNNs), checking robustness of the biological pathway with the RNN, a^nd finding some crucial points for the robustness. An example to apply this method to an apoptosis pathway is also presented.
Languages jpn
Resource Type departmental bulletin paper
Publishers 大島商船高等専門学校
Date Issued 2004-12
File Version Version of Record
Access Rights open access
Relations
[ISSN]0387-9232
[NCID]AN00031668