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Prediction of Protein Secondary Structure Based on a Multi-modal Neural Network: with Modified Profiles of MSA and PSSM
http://hdl.handle.net/10458/281
http://hdl.handle.net/10458/28185ef24de-b567-4e88-987f-5a2473792115
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
KJ00002426419.pdf (990.4 kB)
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| Item type | 紀要論文 / Departmental Bulletin Paper(1) | |||||||||||
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| 公開日 | 2007-06-28 | |||||||||||
| タイトル | ||||||||||||
| タイトル | Prediction of Protein Secondary Structure Based on a Multi-modal Neural Network: with Modified Profiles of MSA and PSSM | |||||||||||
| 言語 | en | |||||||||||
| 言語 | ||||||||||||
| 言語 | eng | |||||||||||
| キーワード | ||||||||||||
| 言語 | en | |||||||||||
| 主題Scheme | Other | |||||||||||
| 主題 | Multi-model neural network, Protein secondary structure, Multiple sequence alignment, Position specific scoring matrix, Majority decision | |||||||||||
| 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 | departmental bulletin paper | |||||||||||
| その他（別言語等）のタイトル | ||||||||||||
| その他のタイトル | Prediction of Protein Secondary Structure Based on a Multi-modal Neural Network: with Modified Profiles of MSA and PSSM | |||||||||||
| 言語 | en | |||||||||||
| 著者 |
Zhu, Hanxi
× Zhu, Hanxi× Yoshihara, Ikuo× 山森, 一人
WEKO
11805
× Yasunaga, Moritoshi |
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| 抄録 | ||||||||||||
| 内容記述タイプ | Abstract | |||||||||||
| 内容記述 | Prediction of protein secondary structure is considered as an important step towards elucidating its three-dimensional structure, as well as its function. We have developed a multi-modal neural network for predicting protein secondary structure. The prediction is based on the frequency profile of multiple sequences alignment and the position specific scoring matrices (PSSM) generated by BLOCK. The multi-modal neural network is composed of two steps: The first step is to develop three neural networks to predict the secondary structure states of proteins: α-helix, β-sheet and non-regular structure respectively. The single-state prediction neural networks use a local input window of consecutive amino acids to predict the secondary structure state of the amino acid located at the center of the input window; The second step is to develop a decision neural network to combine all of the single-state predictions to obtain an overall prediction on three states. This method gives an overall accuracy of 67.8% when using seven-fold cross-validation on a database of 126 non-homologous proteins. To improve the accuracy further, majority decision is introduced to each network for single-state prediction in the first step. By using majority decision, the overall accuracy is improved to 70.2% with corresponding Matthews' correlation coefficients Cα =0.61, Cβ=0.48. |
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| 言語 | en | |||||||||||
| 書誌情報 |
ja : 宮崎大学工学部紀要 en : Memoirs of Faculty of Engineering, University of Miyazaki 巻 32, p. 295-302, 発行日 2003-07 |
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| 出版者 | ||||||||||||
| 出版者 | 宮崎大学工学部 | |||||||||||
| 言語 | ja | |||||||||||
| 出版者 | ||||||||||||
| 出版者 | Faculty of Engineering, University of Miyazaki | |||||||||||
| 言語 | en | |||||||||||
| ISSN | ||||||||||||
| 収録物識別子タイプ | ISSN | |||||||||||
| 収録物識別子 | 05404924 | |||||||||||
| 書誌レコードID | ||||||||||||
| 収録物識別子タイプ | NCID | |||||||||||
| 収録物識別子 | AA00732558 | |||||||||||
| 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 | |||||||||||
