{"created":"2023-05-15T12:13:26.724991+00:00","links":{},"metadata":{"_buckets":{"deposit":"3c384685-e111-4164-b52f-351116f634a5"},"_deposit":{"id":"2796.1","owners":[2],"pid":{"revision_id":0,"type":"depid","value":"2796.1"},"status":"published"},"_oai":{"id":"oai:miyazaki-u.repo.nii.ac.jp:00002796.1","sets":["73","73:36","73:36:330:313"]},"author_link":["14696","11807","11805","12590"],"item_10002_alternative_title_1":{"attribute_name":"その他（別言語等）のタイトル","attribute_value_mlt":[{"subitem_alternative_title":"Prediction of Protein Secondary Structure Based on a Multi-modal Neural Network: with Modified Profiles of MSA and PSSM","subitem_alternative_title_language":"en"}]},"item_10002_biblio_info_7":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2003-07","bibliographicIssueDateType":"Issued"},"bibliographicPageEnd":"302","bibliographicPageStart":"295","bibliographicVolumeNumber":"32","bibliographic_titles":[{"bibliographic_title":"宮崎大学工学部紀要","bibliographic_titleLang":"ja"},{"bibliographic_title":"Memoirs of Faculty of Engineering, University of Miyazaki","bibliographic_titleLang":"en"}]}]},"item_10002_description_5":{"attribute_name":"抄録","attribute_value_mlt":[{"subitem_description":"Prediction of protein secondary structure is considered as an important step towards elucidating\nits three-dimensional structure, as well as its function. We have developed a multi-modal neural\nnetwork for predicting protein secondary structure. The prediction is based on the frequency profile of\nmultiple sequences alignment and the position specific scoring matrices (PSSM) generated by\nBLOCK. The multi-modal neural network is composed of two steps: The first step is to develop three\nneural networks to predict the secondary structure states of proteins: α-helix, β-sheet and\nnon-regular structure respectively. The single-state prediction neural networks use a local input\nwindow of consecutive amino acids to predict the secondary structure state of the amino acid located\nat the center of the input window; The second step is to develop a decision neural network to combine\nall of the single-state predictions to obtain an overall prediction on three states. This method gives an\noverall accuracy of 67.8% when using seven-fold cross-validation on a database of 126\nnon-homologous proteins. To improve the accuracy further, majority decision is introduced to each\nnetwork for single-state prediction in the first step. By using majority decision, the overall accuracy is\nimproved to 70.2% with corresponding Matthews' correlation coefficients Cα =0.61, Cβ=0.48.","subitem_description_language":"en","subitem_description_type":"Abstract"}]},"item_10002_publisher_8":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"宮崎大学工学部","subitem_publisher_language":"ja"},{"subitem_publisher":"Faculty of Engineering, University of Miyazaki","subitem_publisher_language":"en"}]},"item_10002_source_id_11":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA00732558","subitem_source_identifier_type":"NCID"}]},"item_10002_source_id_9":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"05404924","subitem_source_identifier_type":"ISSN"}]},"item_10002_version_type_20":{"attribute_name":"著者版フラグ","attribute_value_mlt":[{"subitem_version_resource":"http://purl.org/coar/version/c_970fb48d4fbd8a85","subitem_version_type":"VoR"}]},"item_creator":{"attribute_name":"著者","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Zhu, Hanxi","creatorNameLang":"en"}],"nameIdentifiers":[{"nameIdentifier":"14696","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"Yoshihara, Ikuo","creatorNameLang":"en"}],"nameIdentifiers":[{"nameIdentifier":"11807","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"Yamamori, Kunihito","creatorNameLang":"en"}],"nameIdentifiers":[{"nameIdentifier":"11805","nameIdentifierScheme":"WEKO"},{"nameIdentifier":"50293395","nameIdentifierScheme":"e-Rad","nameIdentifierURI":"https://kaken.nii.ac.jp/ja/search/?qm=50293395"}]},{"creatorNames":[{"creatorName":"Yasunaga, Moritoshi","creatorNameLang":"en"}],"nameIdentifiers":[{"nameIdentifier":"12590","nameIdentifierScheme":"WEKO"}]}]},"item_files":{"attribute_name":"ファイル情報","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_date","date":[{"dateType":"Available","dateValue":"2020-06-21"}],"displaytype":"detail","filename":"KJ00002426419.pdf","filesize":[{"value":"990.4 kB"}],"format":"application/pdf","licensetype":"license_note","mimetype":"application/pdf","url":{"label":"KJ00002426419.pdf","url":"https://miyazaki-u.repo.nii.ac.jp/record/2796.1/files/KJ00002426419.pdf"},"version_id":"84a0bb31-d342-4d24-b3a0-38f604c9f780"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"Multi-model neural network, Protein secondary structure, Multiple sequence alignment, Position specific scoring matrix, Majority decision","subitem_subject_language":"en","subitem_subject_scheme":"Other"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourcetype":"departmental bulletin paper","resourceuri":"http://purl.org/coar/resource_type/c_6501"}]},"item_title":"Prediction of Protein Secondary Structure Based on a Multi-modal Neural Network: with Modified Profiles of MSA and PSSM","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Prediction of Protein Secondary Structure Based on a Multi-modal Neural Network: with Modified Profiles of MSA and PSSM","subitem_title_language":"en"}]},"item_type_id":"10002","owner":"2","path":["36","73","313"],"pubdate":{"attribute_name":"公開日","attribute_value":"2007-06-28"},"publish_date":"2007-06-28","publish_status":"0","recid":"2796.1","relation_version_is_last":true,"title":["Prediction of Protein Secondary Structure Based on a Multi-modal Neural Network: with Modified Profiles of MSA and PSSM"],"weko_creator_id":"2","weko_shared_id":2},"updated":"2023-07-29T11:18:12.936035+00:00"}