Presentation | 2001/2/2 Learning Protein Structures and Expressing State Space using a Recurrent Neural Network Shigeru SAITO, Hiroyuki SHIOYA, Tsutomu DA-TE, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | A Recurrent Neural Network(RNN)has reflexive structures and an ability of learning Finite State Machines(FSMs). It is known that a state graph of an FSM is extracted in the state space of the trained RNN optimally. In this paper, we use an RNN in order to learn protein secondary structures(alpha-helix and etc). We propose learning methods which reflect properties of protein structures and RNNs, and show that a grammatical structure of an amino acid sequence is acquired in the same way. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Recurrent Neural Network / state space / protein structures / α-helix |
Paper # | NC2000-94 |
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Committee | NC |
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Conference Date | 2001/2/2(1days) |
Place (in Japanese) | (See Japanese page) |
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Topics (in Japanese) | (See Japanese page) |
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Registration To | Neurocomputing (NC) |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Learning Protein Structures and Expressing State Space using a Recurrent Neural Network |
Sub Title (in English) | |
Keyword(1) | Recurrent Neural Network |
Keyword(2) | state space |
Keyword(3) | protein structures |
Keyword(4) | α-helix |
1st Author's Name | Shigeru SAITO |
1st Author's Affiliation | Graduate School of Engineering, Hokkaido University() |
2nd Author's Name | Hiroyuki SHIOYA |
2nd Author's Affiliation | Graduate School of Engineering, Hokkaido University |
3rd Author's Name | Tsutomu DA-TE |
3rd Author's Affiliation | Graduate School of Engineering, Hokkaido University |
Date | 2001/2/2 |
Paper # | NC2000-94 |
Volume (vol) | vol.100 |
Number (no) | 618 |
Page | pp.pp.- |
#Pages | 7 |
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