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Titlebook: Methods in Protein Sequence Analysis; Kazutomo Imahori,Fumio Sakiyama Book 1993 Springer Science+Business Media New York 1993 Amino acid.P

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書目名稱Methods in Protein Sequence Analysis
編輯Kazutomo Imahori,Fumio Sakiyama
視頻videohttp://file.papertrans.cn/633/632331/632331.mp4
圖書封面Titlebook: Methods in Protein Sequence Analysis;  Kazutomo Imahori,Fumio Sakiyama Book 1993 Springer Science+Business Media New York 1993 Amino acid.P
描述The Ninth International Conference on Methods in Protein Sequence Analysis was held for the first time in Asia from September 20 to September 24, 1992 in Otsu (a city near Kyoto), Japan. Approximately 400 delegates attended the meeting. Forty papers were presented orally and 147 poster presentations were discussed. Academic sessions were held from early in the morning until late in the evening. We are confident that the Conference was successful in providing up-to-date information about methods in protein sequence analysis to all participants. Moreover, with the knowledge and understanding of the present standard of various methods of analysis that are being used and will be used, we were able to clarify areas that need to be evaluated, to be improved and be explored further. Major topics in the Conference mostly covered areas in the methodology of protein sequence analysis, such as: micropreparation and microsequencing of proteins, mass spectrometry, post-translational modification, prediction and database analysis, and analysis of protein structures of special interests. The evolution of genetic engineering in molecular biology has greatly accelerated the accumulation of knowledg
出版日期Book 1993
關(guān)鍵詞Amino acid; Polypeptide; biochemistry; chemistry; chromatography; genetic engineering; preparation; protein
版次1
doihttps://doi.org/10.1007/978-1-4899-1603-7
isbn_softcover978-1-4899-1605-1
isbn_ebook978-1-4899-1603-7
copyrightSpringer Science+Business Media New York 1993
The information of publication is updating

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Susumu Tsunasawa,Hisashi Hiranothe complex faults and variable working conditions of the planetary gearbox, and the accuracy is 97%. It is verified that the multi-SoftMax convolutional neural network has strong generalization ability and the advantages of the fault tree structure.
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Akira Tsugita,Keiji Takamoto,Hiromoto Iwadate,Masaharu Kamo,Hiroyuki Yano,Norifumi Miyatake,Kazuo Sathe complex faults and variable working conditions of the planetary gearbox, and the accuracy is 97%. It is verified that the multi-SoftMax convolutional neural network has strong generalization ability and the advantages of the fault tree structure.
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