Presentation	2001/2/2 Learning Protein Structures and Expressing State Space using a Recurrent Neural Network Shigeru SAITO, Hiroyuki SHIOYA, Tsutomu DA-TE,
PDF Download Page	PDF download Page Link
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
Date of Issue

Conference Information
Committee	NC
Conference Date	2001/2/2(1days)
Place (in Japanese)	(See Japanese page)
Place (in English)
Topics (in Japanese)	(See Japanese page)
Topics (in English)
Chair
Vice Chair
Secretary
Assistant

Paper Information
Registration To	Neurocomputing (NC)
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
Date of Issue