書誌事項
- タイトル別名
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- Prediction of debacle parts for robustness in a cell by using recurrent neural networks
- リカレント ニューラル ネットワーク オ モチイタ サイボウナイ ハンノウ システム ニ オケル ロバストネス ガカイ ブイ ヨソク
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説明
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.
収録刊行物
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- 大島商船高等専門学校紀要
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大島商船高等専門学校紀要 37 1-7, 2004-12
大島商船高等専門学校
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詳細情報 詳細情報について
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- CRID
- 1050282812598569984
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- NII論文ID
- 120005437357
- 110004302362
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- NII書誌ID
- AN00031668
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- ISSN
- 03879232
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- NDL書誌ID
- 7196344
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- 本文言語コード
- ja
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- 資料種別
- departmental bulletin paper
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- データソース種別
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- IRDB
- NDL
- CiNii Articles