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Note: Links lead to the DBLP on the Web. Nobuhisa Ueda Nobuhisa Ueda, Kiyoko F. Aoki-Kinoshita , Atsuko Yamaguchi , Tatsuya Akutsu , Hiroshi Mamitsuka : A Probabilistic Model for Mining Labeled Ordered Trees: Capturing Patterns in Carbohydrate Sugar Chains. IEEE Trans. Knowl. Data Eng. 17 (8): 1051-1064 (2005) Pierre Mahé , Nobuhisa Ueda, Tatsuya Akutsu , Jean-Luc Perret , Jean-Philippe Vert : Extensions of marginalized graph kernels. ICML 2004 Kiyoko F. Aoki , Nobuhisa Ueda, Atsuko Yamaguchi , Minoru Kanehisa , Tatsuya Akutsu , Hiroshi Mamitsuka : Application of a new probabilistic model for recognizing complex patterns in glycans. ISMB/ECCB (Supplement of Bioinformatics) 2004 : 6-14 Nobuhisa Ueda, Kiyoko Aoki , Hiroshi Mamitsuka : A General Probabilistic Framework for Mining Labeled Ordered Trees. SDM 2004 Jose C. Nacher , Nobuhisa Ueda, Takuji Yamada , Minoru Kanehisa , Tatsuya Akutsu : Clustering under the line graph transformation: application to reaction network. BMC Bioinformatics 5 : 207 (2004) Hiroto Saigo , Jean-Philippe Vert , Nobuhisa Ueda, Tatsuya Akutsu : Protein homology detection using string alignment kernels. Bioinformatics 20 (11): 1682-1689 (2004) Kiyoko F. Aoki , Atsuko Yamaguchi , Nobuhisa Ueda, Tatsuya Akutsu , Hiroshi Mamitsuka , Susumu Goto , Minoru Kanehisa : KCaM (KEGG Carbohydrate Matcher): a software tool for analyzing the structures of carbohydrate sugar chains. Nucleic Acids Research 32 (Web-Server-Issue): 267-272 (2004) Kiyoko F. Aoki , Nobuhisa Ueda, Atsuko Yamaguchi , Tatsuya Akutsu , Minoru Kanehisa , Hiroshi Mamitsuka : Managing and Analyzing Carbohydrate Data. SIGMOD Record 33 (2): 33-38 (2004) Morihiro Hayashida , Nobuhisa Ueda, Tatsuya Akutsu : Inferring strengths of protein-protein interactions from experimental data using linear programming. ECCB 2003 : 58-65 Nobuhisa Ueda, Taisuke Sato : Simplified Training Algorithms for Hierarchical Hidden Markov Models. Discovery Science 2001 : 401-415 Yoshitaka Kameya , Nobuhisa Ueda, Taisuke Sato : A Graphical Method for Parameter Learning of Symbolic-Statistical Models. Discovery Science 1999 : 264-276 1 [ 3 ] [ 4 ] [ 5 ] [ 6 ] [ 7 ] [ 9 ] [ 10 ] [ 11 ] 2 [ 8 ] 3 [ 4 ] [ 5 ] [ 9 ] 4 [ 11 ] 5 [ 5 ] 6 [ 3 ] 7 [ 1 ] 8 [ 4 ] [ 5 ] [ 7 ] [ 9 ] 9 [ 10 ] 10 [ 4 ] [ 5 ] [ 8 ] [ 9 ] [ 11 ] 11 [ 7 ] 12 [ 10 ] 13 [ 6 ] 14 [ 1 ] [ 2 ] 15 [ 6 ] [ 10 ] 16 [ 7 ] 17 [ 4 ] [ 5 ] [ 9 ] [ 11 ]  ©2006 Association for Computing Machinery |
