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A method of classification and recognition of blue copper protein
https://doi.org/10.24517/00010141
https://doi.org/10.24517/00010141f2c402cd-50e5-4649-b699-33f085c12138
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
SC-PR-NISHIKAWA-K-003.pdf (332.0 kB)
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| Item type | 学術雑誌論文 / Journal Article(1) | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| 公開日 | 2017-10-03 | |||||||||
| タイトル | ||||||||||
| タイトル | A method of classification and recognition of blue copper protein | |||||||||
| 言語 | ||||||||||
| 言語 | eng | |||||||||
| 資源タイプ | ||||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||||||
| 資源タイプ | journal article | |||||||||
| ID登録 | ||||||||||
| ID登録 | 10.24517/00010141 | |||||||||
| ID登録タイプ | JaLC | |||||||||
| 著者 |
Purqon, Acep
× Purqon, Acep× Sugiyama, Ayumu× Takamatsu, Yuichiro× Nagao, Hidemi× Nishikawa, Kiyoshi |
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| 著者別表示 |
長尾, 秀実
× 長尾, 秀実
× 西川, 清
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| 提供者所属 | ||||||||||
| 内容記述タイプ | Other | |||||||||
| 内容記述 | 金沢大学大学院自然科学研究科計算科学 | |||||||||
| 提供者所属 | ||||||||||
| 内容記述タイプ | Other | |||||||||
| 内容記述 | 金沢大学理学部 | |||||||||
| 書誌情報 |
AIP Conference Proceedings 号 832, p. 574-577, 発行日 2006-05-01 |
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| ISSN | ||||||||||
| 収録物識別子タイプ | ISSN | |||||||||
| 収録物識別子 | 0094-243X | |||||||||
| DOI | ||||||||||
| 関連タイプ | isIdenticalTo | |||||||||
| 識別子タイプ | DOI | |||||||||
| 関連識別子 | https://doi.org/10.1063/1.2204568 | |||||||||
| 出版者 | ||||||||||
| 出版者 | American Institute of Physics | |||||||||
| 抄録 | ||||||||||
| 内容記述タイプ | Abstract | |||||||||
| 内容記述 | Some proteins in blue copper proteins have similar properties. In some cases it is not easy to distinguish the proteins each other. The study to recognize and classify in blue copper proteins has important roles to recognize the difference of similar properties, for examples, structures and residue sequences in blue copper proteins. There are many methods being developed to predict protein structure from many approachs, which one still not satisfactory yet. Therefore it is a challenge for scientists to develop or improve their methods. One of promising method is artificial neural networks (ANN). ANN is learning machine methods consisted of input, hidden and output layer. ANN is tested to recognize secondary structure in blue copper protein. It is found that ANN can distinguish for 7-type of secondary structure and recognize 72% secondary structure in blue copper protein. © 2006 American Institute of Physics. | |||||||||
| 著者版フラグ | ||||||||||
| 出版タイプ | VoR | |||||||||
| 出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||||||
