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Supercomputer
Results Achieved by Using the Supercomputer (Published Papers)
Published Papers List before 2008

Supercomputer

If there are any additional papers that have not been included in the above list, please contact us at the e-mail address below. Thank you.
support@hgc.jp.

Published Papers List in 2014.
Published Papers List in 2013.
Published Papers List in 2012.
Published Papers List in 2011.
Published Papers List in 2010.
Published Papers List in 2009.
Published Papers List in 2008.
Published Papers List before 2008.

Published Papers List before 2008

Author	Title	Journal/Book
Affara M, Dunmore B, Savoie C, Imoto S, Tamada Y, Araki H, Charnock-Jones DS, Miyano S, Print C	Understanding endothelial cell apoptosis: what can the transcriptome, glycome and proteome reveal?	Philos Trans R Soc Lond B Biol Sci. 2007 Aug 29;362(1484):1469-87.
Akiyama F, Tanaka T, Yamada R, Ohnishi Y, Tsunoda T, Maeda S, Takei T, Obara W, Ito K, Honda K, Uchida K, Tsuchiya K, Nitta K, Yumura W, Nihei H, Ujiie T, Nagane Y, Miyano S, Suzuki Y, Fujioka T, Narita I, Gejyo F, Nakamura Y	Single-nucleotide polymorphisms in the class II region of the major histocompatibility complex in Japanese patients with immunoglobulin A nephropathy	J Hum Genet. 2002;47(10):532-8.
Akutsu T, Bannai H, Miyano S, Ott S	On the complexity of deriving position specific score matrices from positive and negative sequences	Discrete Applied Mathematics, 155, 676-685, 2007. Proceedings of 13th Annual Symposium on Combinatorial Pattern Matching (CPM 2002). Lecture Notes in Computer Science, 2373, 168-177, 2002
Akutsu T, Kuhara S, Maruyama O, Miyano S	A System for Identifying Genetic Networks from Gene Expression Patterns Produced by Gene Disruptions and Overexpressions	Genome Inform Ser Workshop Genome Inform. 1998;9:151-160.
Akutsu T, Kuhara S, Maruyama O, Miyano S	Identification of genetic networks by strategic gene disruptions and gene overexpressions under a boolean model	Theoretical Computer Science. 298(1):235-251, 2003. Proceedings of 9th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA'98), 695-702, 1998
Akutsu T, Miyano S	On the approximation of protein threading	Theoretical Computer Science, 210, 261-275, 1999. Proceedings of the First Annual International Conference on Research in Computational Molecular Biology. 3-8, 1997
Akutsu T, Miyano S	Selecting informative genes for cancer classification using gene expression data	Computational and Statistical Approaches to Genomics (W. Zhang and I. Shmulevich eds.), Kluwer Academic Pub, 79-92, 2002
Akutsu T, Miyano S, Kuhara S	A simple greedy algorithm for finding functional relations: efficient implementation and average case analysis	Theoretical Computer Science, 292(2), 481-495, 2003. Proceedings of Third International Conference on Discovery Science. Lecture Notes in Artificial Intelligence, 1967, 86-98, 2000
Akutsu T, Miyano S, Kuhara S	Algorithms for identifying Boolean networks and related biological networks based on matrix multiplication and fingerprint function	J Comput Biol. 2000;7(3-4):331-43.
Akutsu T, Miyano S, Kuhara S	Algorithms for inferring qualitative models of biological networks	Pac Symp Biocomput. 2000:293-304.
Akutsu T, Miyano S, Kuhara S	Identification of genetic networks from a small number of gene expression patterns under the Boolean network model	Pac Symp Biocomput. 1999:17-28.
Akutsu T, Miyano S, Kuhara S	Inferring qualitative relations in genetic networks and metabolic pathways	Bioinformatics. 2000 Aug;16(8):727-34.
Ando T, Imoto S, Konishi S	Adaptive learning machines for nonlinear classification and Bayesian information criterion	Bulletin of Informatics and Cybernetics, 36, 147-162, 2004
Ando T, Imoto S, Miyano S	Bayesian network and radial basis function network regression for nonlinear modeling of genetic network	Proc. Third International Conference on Information, 561-564, 2004
Ando T, Imoto S, Miyano S	Functional data analysis of the dynamics of gene regulatory networks	Proc. Knowledge Exploration in Life Science Informatics KELSI2004. Lecture Notes in Artificial Intelligence, 3303, 69-83, 2004
Ando T, Imoto S, Miyano S	Kernel mixture survival models for identifying cancer subtypes, predicting patient's cancer types and survival probabilities	Genome Inform. 2004;15(2):201-10
Araki Y, Konishi S, Imoto S	Functional discriminant analysis for time-seriese gene expression data via radial basis function expansion	Proc. COMPSTAT 2004. 613-62Physica-Verlag/Springer, 2004. (COMPSTAT2004: Refereed conference)
Arikawa S, Haraguchi M, Inoue H, Kawasaki Y, Miyahara T, Miyano S, Oshima, K, Sakai H, Shinohara T, Shiraishi S, Takeda M, Takeya S, Yamamoto A, Yuasa H	The text database management system SIGMA: an improvement of the main engine	Proceedings of Berliner Informatik-Tage, 72-81, 1989
Arikawa S, Kuhara S, Miyano S, Shinohara A, Shinohara T	A learning algorithm for elementary formal systems and its experiments on identification of transmembrane domains	Proceedings of the Twenty-Fifth Hawaii International Conference on System Science, Vol.I. IEEE Computer Society Press. 675-684, 1992
Arikawa S, Miyano S, Shinohara A, Shimozono S, Shinohara T, Kuhara S	Knowledge acquisition from amino acid sequences by learning algorithms.	Proceedings of the Second Japanese Knowledge Acquisition for Knowledge-Based Systems Workshop, 109-128, 1992
Arikawa S, Miyano S, Shinohara A, Shinohara T, Yamamoto A	Algorithmic learning theory with elementary formal systems	IEICE Transactions on Information and Systems, E75-D(4), 405-414, 1992
Arikawa S, Shinohara T, Miyano S, Shinohara A	More about learning elementary formal systems	Proceedings of Second International Workshop on Nonmonotonic and Inductive Logic. Lecture Notes in Computer Science, 659, 107-117, 1993
Bannai H, Hyyro H, Shinohara A, Takeda M, Nakai K, Miyano S	An O(N2) algorithm for discovering optimal Boolean pattern pairs	IEEE/ACM Trans Comput Biol Bioinform. 2004 Oct-Dec;1(4):159-70.
Bannai H, Inenaga S, Shinohara A, Takeda M	Inferring strings from graphs and arrays	Proc. 28th International Symposium on Mathematical Foundations of Computer Science (MFCS2003). Lecture Notes in Computer Science, 2747, 208-217, 2003. (Peer-reviewed paper)
Bannai H, Inenaga S, Shinohara A, Takeda M, Miyano S	A string pattern regression algorithm and its application to pattern discovery in long introns	Genome Inform. 2002;13:3-11.
Bannai H, Inenaga S, Shinohara A, Takeda M, Miyano S	Efficiently finding regulatory elements using correlation with gene expression	J Bioinform Comput Biol. 2004 Jun;2(2):273-88.
Bannai H, Miyano S	A definition of discovery in terms of generalized descriptional complexity	Proceedings of Second International Conference on Discovery Science. Lecture Notes in Artificial Intelligence, 1721, 316-318, 1999
Bannai H, Tamada Y, Maruyama O, Miyano S	HypothesisCreator: Concepts for accelerating the computational knowledge discovery process.	Electronic Transactions on Artificial Intelligence, 5, 73-83, 2001
Bannai H, Tamada Y, Maruyama O, Miyano S	VML: a view modeling language for computational knowledge discovery	Proceedings of Fourth International Conference on Discovery Science. Lecture Notes in Artificial Intelligence, 2226, 30-44, 2001
Bannai H, Tamada Y, Maruyama O, Nakai K, Miyano S	Extensive feature detection of N-terminal protein sorting signals	Bioinformatics. 2002 Feb;18(2):298-305.
Bannai H, Tamada Y, Maruyama O, Nakai K, Miyano S	Views: fundamental building blocks in the process of knowledge discovery.	Proceedings of the 14th International FLAIRS Conference. AAAI Press, 233-238, 2001
de Hoon M, Imoto S, Kobayashi K, Ogasawara N, Miyano S	Inferring gene regulatory networks from time-ordered gene expression data of Bacillus subtilis using differential equations	Pac Symp Biocomput. 2003:17-28.
De Hoon MJ, Imoto S, Kobayashi K, Ogasawara N, Miyano S	Inferring gene regulatory networks from time-ordered gene expression data using differential equations	Proceedings of Fifth International Conference on Discovery Science. Lecture Notes in Artificial Intelligence, 2534, 267-274, 2002
De Hoon MJ, Imoto S, Kobayashi K, Ogasawara N, Miyano S	Predicting the operon structure of Bacillus subtilis using operon length, intergene distance, and gene expression information	Pac Symp Biocomput. 2004:276-87
De Hoon MJ, Imoto S, Miyano S	Statistical analysis of a small set of time-ordered gene expression data using linear splines	Bioinformatics. 2002 Nov;18(11):1477-85.
De Hoon MJ, Imoto S, Nolan J, Miyano S	Open source clustering software.	Bioinformatics, 20(9), 1453-1454, 2004
De Hoon MJ, Makita Y, Imoto S, Kobayashi K, Ogasawara N, Nakai K, Miyano S	Predicting gene regulation by sigma factors in Bacillus subtilis from genome-wide data	Bioinformatics. 2004 Aug 4;20 Suppl 1:i101-8.
De Hoon MJ, Makita Y, Nakai K, Miyano S	Prediction of transcriptional terminators in Bacillus subtilis and related species	PLoS Comput Biol. 2005 Aug;1(3):e25. Epub 2005 Aug 12.
Doi A, Fujita S, Matsuno H, Nagasaki M, Miyano S	Constructing biological pathway models with hybrid functional Petri nets	In Silico Biol. 2004;4(3):271-91
Doi A, Nagasaki M, Fujita S, Matsuno H, Miyano S	Genomic Object Net: II. Modelling biopathways by hybrid functional Petri net with extension	Appl Bioinformatics. 2003;2(3):185-8.
Doi A, Nagasaki M, Matsuno H, Miyano S	Simulation-based validation of the p53 transcriptional activity with hybrid functional petri net	In Silico Biol. 2006;6(1-2):1-13.
Doi A, Nagasaki M, Ueno K, Matsuno H, Miyano S	A combined pathway to simulate CDK-dependent phosphorylation and ARF-dependent stabilization for p53 transcriptional activity	Genome Inform. 2006;17(1):112-23.
Fujita A, Sato JR, Garay-Malpartida HM, Yamaguchi R, Miyano S, Sogayar MC, Ferreira CE	Modeling gene expression regulatory networks with the sparse vector autoregressive model	BMC Syst Biol. 2007 Aug 30;1:39.
Fujita S, Matsui M, Matsuno H, Miyano S	Modeling and simulation of fission yeast cell cycle on hybrid functional Petri net	IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, E87-A(11), 2919-2928, 2004
Fukuda K, Yamamoto S, Sakai N, Nakamura H, Nakanishi Y, Takagi T	"Graphical syntax and query for pathway database." The 10th World Multi-Conference on Systemics,	Cybernetics and Informatics proceedings (WMSCI2006), Vol.IV, 7-10, ISBN:980-6560-65-5, 2006
Furukawa N, Matsumoto S, Shinohara A, Shoudai T, Miyano S	HAKKE: A multi-strategy prediction system for sequences	Genome Informatics, 7, 98-107, 1996
Furuya S, Miyano S	Analogy is NP-hard	Proceedings of the Second Workshop on Algorithmic Learning Theory, 207-212, 1991
Furuya S, Miyano S	NP-complete problems on label updating calculation in ATMS	Bulletin of Informatics and Cybernetics, 25(1-2), 1-5, 1992
Furuya S, Miyano S	NP-hard aspects in analogical reasoning	Bulletin of Informatics and Cybernetics, 25(3-4), 155-159, 1993
Gupta PK, Yoshida R, Imoto S, Yamaguchi R, Miyano S	Statistical absolute evaluation of gene ontology terms with gene expression data	Lecture Notes in Bioinformatics, 4463, 146-157, 2007
Hayashi T, Miyano S	Finite tree automata on infinite trees	Bulletin of Informatics and Cybernetics, 21(3-4), 71-82, 1985
Hirokawa S, Miyano S	A note on the regularity of fuzzy languages	Memoirs of the Faculty of Science, Kyushu University, Ser. A. 32(1), 61-66, 1978
Hirose O, Nariai N, Tamada Y, Bannai H, Imoto S, Miyano S	Estimating gene networks from expression data and binding location data via boolean networks	Lecture Notes in Computer Science, 3482, 349-356, 2005. Proc. 1st International Conference on Computational Science and Its Applications (Workshop on Data Mining and Bioinformatics)
Hirose O, Yoshida R, Yamaguchi R, Imoto S, Higuchi T, Miyano S	Clustering samples characterized by time course gene expression profiles using the mixture of state space models	Genome Inform. 2007;18:258-66.
Imoto S	Knowledge discovery of causal relations among genes from microarray gene expression data (in Japanese with English abstract)	Journal of Japan Statistical Sciety, 37(1), 55-70, 2007
Imoto S, Goto T, Miyano S	Estimation of genetic networks and functional structures between genes by using Bayesian networks and nonparametric regression	Pac Symp Biocomput. 2002:175-86.
Imoto S, Higuchi T, Goto T, Miyano S	Error tolerant model for incorporating biological knowledge with expression data in estimating gene networks	Statistical Methodology, 3(1), 1-16, 2006
Imoto S, Higuchi T, Goto T, Tashiro K, Kuhara S, Miyano S	Combining microarrays and biological knowledge for estimating gene networks via Bayesian networks	Proc IEEE Comput Soc Bioinform Conf. 2003;2:104-13. J Bioinform Comput Biol. 2004 Mar;2(1):77-98.
Imoto S, Higuchi T, Kim S, Jeong E, Miyano S	Residual bootstrapping and median filtering for robust estimation of gene networks from microarray data	Proc. 2nd Computational Methods in Systems Biology. Lecture Notes in Bioinformatics, 3082, 149-162004
Imoto S, Kim S, Goto T, Miyano S, Aburatani S, Tashiro K, Kuhara S	Bayesian network and nonparametric heteroscedastic regression for nonlinear modeling of genetic network	J Bioinform Comput Biol. 2003 Jul;1(2):231-52.
Imoto S, Konishi S	Selection of smoothing parameters in B-spline nonparametric regression models using information criteria	Annals of the Institute of Statistical Mathematics, 55(4), 671-687, 2003
Imoto S, Savoie CJ, Aburatani S, Kim S, Tashiro K, Kuhara S, Miyano S	Use of gene networks for identifying and validating drug targets	J Bioinform Comput Biol. 2003 Oct;1(3):459-74.
Imoto S, Tamada Y, Araki H, Yasuda K, Print CG, Charnock-Jones SD, Sanders D, Savoie CJ, Tashiro K, Kuhara S, Miyano S	Computational strategy for discovering druggable gene networks from genome-wide RNA expression profiles	Pac Symp Biocomput. 2006:559-71.
Imoto S, Tamada Y, Savoie CJ, Miyano S	Analysis of gene networks for drug target discovery and validation	Methods Mol Biol. 2007;360:33-56.
Inenaga S, Bannai H, Hyyro H, Shinohara A, Takeda M, Nakai K, Miyano S	Finding optimal pairs of cooperative and competing patterns with bounded distance	Proc. 7th International Conference on Discovery Science (DS 2004). Lecture Notes in Artificial Intelligence, 3245:32-46, 2004
Inenaga S, Bannai H, Shinohara A, Takeda M, Arikawa S	Discovering best variable-length-don't-care patterns	Proc. 5th International Conference on Discovery Science (DS2002). Lecture Notes in Computer Science, 2534: 86-97, 2002. (Peer-reviewed paper)
Inenaga S, Shinohara A, Takeda M, Bannai H, Arikawa S	Space-economical construction of index structures for all-suffixes of a string	Proc. 27th. International Symposium on Mathematical Foundation of Computer Science (MFCS2002), Lecture Notes in Computer Science, 242341-352, 2002. (Peer-reviewed paper)
Jeong E, Chung IF, Miyano S	A neural network method for identification of RNA-interacting residues in protein	Genome Inform. 2004;15(1):105-16.
Jeong E, Miyano S	A weighted profile based method for protein-RNA interacting residue prediction	Transactions on Computational Systems Biology. Lecture Notes in Computer Science, 3939, 123-139, 2006
Jeong E, Nagasaki M, Miyano S	Conversion from BioPAX to CSO for system dynamics and visualization of biological pathway	Genome Inform. 2007;18:225-36.
Jeong E, Nagasaki M, Saito A, Miyano S	Cell system ontology: representation for modeling, visualizing, and simulating biological pathways	In Silico Biol. 2007;7(6):623-38.
Kato M, Nagasaki M, Doi A, Miyano S	Automatic drawing of biological networks using cross cost and subcomponent data	Genome Inform. 2005;16(2):22-31.
Kim S, Imoto S, Miyano S	Dynamic Bayesian network and nonparametric regression for nonlinear modeling of gene networks from time series gene expression data	Biosystems. 2004 Jul;75(1-3):57-65.
Kim SY, Imoto S, Miyano S	Inferring gene networks from time series microarray data using dynamic Bayesian networks	Brief Bioinform. 2003 Sep;4(3):228-35.
Kitakaze H, Matsuno H, Ikeda N, Miyano S	Prediction of debacle points for robustness of biological pathways by using recurrent neural networks	Genome Inform. 2005;16(1):192-202.
Kojima K, Nagasaki M, Jeong E, Kato M, Miyano S	An efficient grid layout algorithm for biological networks utilizing various biological attributes	BMC Bioinformatics. 2007 Mar 6;8:76.
Konishi S, Ando T, Imoto S	Bayesian information criteria and smoothing parameter selection in radial basis function networks	Biometrika, 91(1), 27-43, 2003
Kushida T, Takagi T, Fukuda KI	Event ontology: a pathway-centric ontology for biological processes	Pac Symp Biocomput. 2006:152-63.
Li C, Ge QW, Nakata M, Matsuno H, Miyano S	Modelling and simulation of signal transductions in an apoptosis pathway by using timed Petri nets	J Biosci. 2007 Jan;32(1):113-27.
Li C, Suzuki S, Ge QW, Nakata M, Matsuno H, Miyano S	Structural modeling and analysis of signaling pathways based on Petri nets	J Bioinform Comput Biol. 2006 Oct;4(5):1119-40.
Makita Y, De Hoon MJ, Ogasawara N, Miyano S, Nakai K	Bayesian joint prediction of associated transcription factors in Bacillus subtilis	Pac Symp Biocomput. 2005:507-18.
Maruyama O, Bannai H, Tamada Y, Kuhara S, Miyano S	Fast algorithm for extracting multiple unordered short motifs using bit operations	Information Sciences, 146(1-4), 115-126, 2002
Maruyama O, Miyano S	Design sspects of discovery systems	IEICE Transactions on Information and Systems, E83-D(1), 61-70, 2000
Maruyama O, Miyano S	Inferring a tree from walks	Theoretical Computer Science, 161(1-2), 289-300, 1996
Maruyama O, Miyano S	Taking a walk on a graph	Mathematica Japonica, 43(3), 595-606, 1996
Maruyama O, Shoudai T, Furuichi E, Kuhara S, Miyano S	Learning conformation rules	Proceedings of Fourth International Conference on Discovery Science. Lecture Notes in Artificial Intelligence, 2226, 243-257, 2001
Maruyama O, Shoudai T, Miyano S	Toward drawing an atlas of hypothesis classes: approximating a hypothesis via another hypothesis model	Proc. 5th International Conference on Discovery Science (DS2002). Lecture Notes in Computer Science, 2534, 220-232, 2002
Maruyama O, Uchida T, Shoudai T, Miyano S	Toward Genomic Hypothesis Creator: View Designer for Discovery	Proceedings of First International Conference on Discovery Science. Lecture Notes in Artificial Intelligence, 1532, 105-116, 1998
Maruyama O, Uchida T, Sim KL, Miyano S	Designing views in HypothesisCreator: System for assisting in discovery	Proceedings of Second International Conference on Discovery Science. Lecture Notes in Artificial Intelligence, 1721, 115-127, 1999
Matsui M, Fujita S, Suzuki S, Matsuno H, Miyano S	Simulated cell division processes of the Xenopus cell cycle pathway by Genomic Object Net	J. Integrative Bioinformatics, 3, 95-104, 2004
Matsuno H, Doi A, Hirata Y, Miyano S	XML documentation of biopathways and their simulations in Genomic Object Net	Genome Inform. 2001;12:54-62.
Matsuno H, Doi A, Nagasaki M, Miyano S	Hybrid Petri net representation of gene regulatory network	Pac Symp Biocomput. 2000:341-52.
Matsuno H, Fujita S, Doi A, Nagasaki M, Miyano S	Towards biopathway modeling and simulation	Proceedings of 24th International Conference on Applications and Theory of Petri Nets (ICATPN 2003). Lecture Notes in Computer Science, 2679, 3-22, 2003
Matsuno H, Inouye ST, Okitsu Y, Fujii Y, Miyano S	A new regulatory interaction suggested by simulations for circadian genetic control mechanism in mammals	J Bioinform Comput Biol. 2006 Feb;4(1):139-53.
Matsuno H, Li C, Miyano S	Petri net based description for systematic understanding of biological pathways	IEICE Trans. Fundamentals, E89-A(11), 3166-3174, 2006
Matsuno H, Murakami R, Yamane R, Yamasaki N, Fujita S, Yoshimori H, Miyano S	Boundary formation by notch signaling in Drosophila multicellular systems: experimental observations and gene network modeling by Genomic Object Net	Pac Symp Biocomput. 2003:152-63.
Matsuno H, Tanaka Y, Aoshima H, Doi A, Matsui M, Miyano S	Biopathways representation and simulation on hybrid functional Petri net	In Silico Biol. 2003;3(3):389-404.
Miyano S	A hierarchy theorem for multihead stack-counter automata	Acta Informatica, 17(1), 63-67, 1982
Miyano S	A parallelizable lexicographically first edge-induced subgraph problem	Information Processing Letters, 27(2), 75-78, 1988
Miyano S	Computational systems biology	Proc. Third International Conference on Information (Li, L. and Yen, K.K., Eds.), 9-14, 2004
Miyano S	Genome Informatics: New Frontiers of Computer Science and Biosciences	Cooperative Databases and Applications (Advances Database Research and Development Series Vol. 7). edited by Kambayashi Y. and Yokota K. (World Scientific), 12-21, 1997
Miyano S	Indexing alternating finite automata and binary tree like circuits	Bulletin of Informatics and Cybernetics, 23(1-2), 79-88, 1988
Miyano S	Inference, modeling and simulation of gene networks	Proceedings of International Workshop on Computational Methods in Systems Biology. Lecture Notes in Computer Science, 2602, 207-211, 2003
Miyano S	Learning theory towards genome informatics	IEICE Transactions on Information and Systems, E78-D(5), 560-567, 1995 Proc. 4th International Workshop on Algorithmic Learning Theory, Lecture Notes in Computer Science, 744: 19-36, 1993
Miyano S	On a lower bound of Shepherdson function	Memoirs of the Faculty of Science, Kyushu University, Ser. A. 33(2), 257-267, 1979
Miyano S	On an automaton which recognizes a family of automata	Memoirs of the Faculty of Science, Kyushu University, Ser. A. 32(1), 37-51, 1978
Miyano S	One-way weak-stack-counter automata	Journal of Computer and System Sciences, 20(1), 59-76, 1980
Miyano S	Parallel complexity and P-complete problems	Proceedings of International Conference on Fifth Generation Computer Systems 1988, 532-541, 1988
Miyano S	Remarks on multihead pushdown automata and multihead stack automata	Journal of Computer and System Sciences, 27(1), 116-124, 1983
Miyano S	Remarks on two-way automata with weak-counters	Information Processing Letters, 18(2), 105-107, 1984
Miyano S	Systematized approaches to the complexity of subgraph problems	Journal of Information Processing, 13(4), 442-448, 1990
Miyano S	The lexicographically first maximal subgraph problems - P-completeness and NC algorithms	Mathematical Systems Theory, 22(1), 47-73, 1989
Miyano S	Two-way deterministic multi-weak-counter machines	Theoretical Computer Science. 21(1):27-32, 1982
Miyano S	ΔP2-complete lexicographically first maximal subgraph problems	Theoretical Computer Science, 88(1), 33-57, 1991
Miyano S, Haraguchi M	Recovery of incomplete tables under functional dependencies	Bulletin of Informatics and Cybernetics, 20(1-2), 25-41, 1982
Miyano S, Hayashi T	Alternating finite automata on ω-words	Theoretical Computer Science, 32(3), 321-330, 1984
Miyano S, Shimozono S, Maruyama O	Some algorithmic problems arising from genome informatics	Advances in Computing Techniques - Algorithms, Databases and Prallel Processing. World Scientific, 45-59, 1995
Miyano S, Shinohara A, Shinohara T	Polynomial-time learning of elementary formal systems	New Generation Computing, 18(3), 217-242, 2000
Miyano S, Shinohara A, Shinohara T	Which classes of elementary formal systems are polynomial-time learnable?	Proceedings of the Second Workshop on Algorithmic Learning Theory, 139-150, 1991
Moriyama T, Shinohara A, Takeda M, Maruyama O, Goto T, Miyano S, Kuhara S	A System to Find Genetic Networks Using Weighted Network Model	Genome Inform Ser Workshop Genome Inform. 1999;10:186-195.
Nagasaki M, Doi A, Matsuno H, Miyano S	A versatile petri net based architecture for modeling and simulation of complex biological processes	Genome Inform. 2004;15(1):180-97.
Nagasaki M, Doi A, Matsuno H, Miyano S	Computational modeling of biological processes with Petri net based architecture	Bioinformatics Technologies (Y.P. Chen, ed). 179-242, 2005
Nagasaki M, Doi A, Matsuno H, Miyano S	Genomic Object Net: I. A platform for modelling and simulating biopathways	Appl Bioinformatics. 2003;2(3):181-4.
Nagasaki M, Doi A, Matsuno H, Miyano S	Integrating biopathway databases for large-scale modeling and simulation	Proc. Second Asia-Pacific Bioinformatics Conference (APBC2004) (Y.P. Chen, Ed.). Conferences in Research and Practice in Information Technology, 29, 43-52, 2004
Nagasaki M, Doi A, Matsuno H, Miyano S	Petri net modeling of biological pathways	Proc. Algebraic Biology 20(Universal Academy Press), 19-31, 2005
Nagasaki M, Doi A, Matsuno H, Miyano S	Recreating biopathway databases towards simulation	Proceedings of International Workshop on Computational Methods in Systems Biology. Lecture Notes in Computer Science, 2602, 168-169, 2003
Nagasaki M, Onami S, Miyano S, Kitano H	Bio-calculus: Its Concept and Molecular Interaction	Genome Inform Ser Workshop Genome Inform. 1999;10:133-143.
Nagasaki M, Yamaguchi R, Yoshida R, Imoto S, Doi A, Tamada Y, Matsuno H, Miyano S, Higuchi T	Genomic data assimilation for estimating hybrid functional Petri net from time-course gene expression data	Genome Inform. 2006;17(1):46-61.
Nakamichi R, Imoto S, Miyano S	Case-control study of binary trait considering interactions between SNPs and environmental effects using logistic regression	Proc. 4th IEEE Bioinformatics and Bioengineering. IEEE Press, 73-78, 2004
Nakamichi R, Imoto S, Miyano S	Statistical model selection method to analyze combinatorial effects of SNPs and environmental factors for binary disease	International J. Artificial Intelligence Tools, 15(5), 711-724, 2006
Nakano M, Noda R, Kitakaze H, Matsuno H, Miyano S	XML pathway file conversion between Genomic Object Net and SBML	Proc. The Third International Conference on Information, 585-588, 2004
Nakayashiki T, Ebihara K, Bannai H, Nakamura Y	Yeast [PSI+] "prions" that are crosstransmissible and susceptible beyond a species barrier through a quasi-prion state	Mol Cell. 2001 Jun;7(6):1121-30.
Nariai N, Kim S, Imoto S, Miyano S	Using protein-protein interactions for refining gene networks estimated from microarray data by Bayesian networks	Pac Symp Biocomput. 2004:336-47.
Nariai N, Tamada Y, Imoto S, Miyano S	Estimating gene regulatory networks and protein-protein interactions of Saccharomyces cerevisiae from multiple genome-wide data	Bioinformatics. 2005 Sep 1;21 Suppl 2:ii206-12.
Noda K, Shinohara A, Takeda M, Matsumoto S, Miyano S, Kuhara S	Finding Genetic Network from Experiments by Weighted Network Model	Genome Inform Ser Workshop Genome Inform. 1998;9:141-150.
Numata K, Imoto S, Miyano S	A structure learning algorithm for inference of gene networks from microarray gene expression data using Bayesian networks	Proc. IEEE 7th International Symposium on Bioinformatics & Bioengineering, 1280-1284, 2007. (BIBE2007: Refereed conference; Digital Object Identifier 10.1109/BIBE.2007.4375731)
Obara W, Iida A, Suzuki Y, Tanaka T, Akiyama F, Maeda S, Ohnishi Y, Yamada R, Tsunoda T, Takei T, Ito K, Honda K, Uchida K, Tsuchiya K, Yumura W, Ujiie T, Nagane Y, Nitta K, Miyano S, Narita I, Gejyo F, Nihei H, Fujioka T, Nakamura Y	Association of single-nucleotide polymorphisms in the polymeric immunoglobulin receptor gene with immunoglobulin A nephropathy (IgAN) in Japanese patients	J Hum Genet. 2003;48(6):293-9. Epub 2003 May 10.
Ohtsubo S, Iida A, Nitta K, Tanaka T, Yamada R, Ohnishi Y, Maeda S, Tsunoda T, Takei T, Obara W, Akiyama F, Ito K, Honda K, Uchida K, Tsuchiya K, Yumura W, Ujiie T, Nagane Y, Miyano S, Suzuki Y, Narita I, Gejyo F, Fujioka T, Nihei H, Nakamura Y	Association of a single-nucleotide polymorphism in the immunoglobulin mu-binding protein 2 gene with immunoglobulin A nephropathy	J Hum Genet. 2005;50(1):30-5. Epub 2004 Dec 14.
Okada R, Sugii M, Matsuno H, Miyano S	Machine learning prediction of amino acid patterns in protein N-myristoylation	Lecture Notes in Bioinformatics, 4146, 4-14, 2006. (20Workshop on Pattern Recognition In Bioinformatics (PRIB'06))
Onami S, Hamahashi S, Nagasaki M, Miyano S, Kitano H	Automatic acquisition of cell lineage through 4D micorscopy and analysis of early C. elegans embryogenesis	Foundations of Systems Biology, Kitano, H. (ed.), MIT Press, 39-55, 2001
Ott S, Hansen A, Kim SY, Miyano S	Superiority of network motifs over optimal networks and an application to the revelation of gene network evolution	Bioinformatics. 2005 Jan 15;21(2):227-38. Epub 2004 Sep 17.
Ott S, Imoto S, Miyano S	Finding optimal models for small gene networks	Pac Symp Biocomput. 2004:557-67.
Ott S, Miyano S	Finding optimal gene networks using biological constraints	Genome Inform. 2003;14:124-33.
Ott S, Tamada Y, Bannai H, Nakai K, Miyano S	Intrasplicing--analysis of long intron sequences	Pac Symp Biocomput. 2003:339-50.
Saito A, Nagasaki M, Doi A, Ueno K, Miyano S	Cell fate simulation model of gustatory neurons with MicroRNAs double-negative feedback loop by hybrid functional Petri net with extension	Genome Inform. 2006;17(1):100-11.
Saito A, Nagasaki M, Oyama M, Kozuka-Hata H, Semba K, Sugano S, Yamamoto T, Miyano S	AYUMS: an algorithm for completely automatic quantitation based on LC-MS/MS proteome data and its application to the analysis of signal transduction	BMC Bioinformatics. 2007 Jan 18;8:15.
Savoie CJ, Aburatani S, Watanabe S, Eguchi Y, Muta S, Imoto S, Miyano S, Kuhara S, Tashiro K	Use of gene networks from full genome microarray libraries to identify functionally relevant drug-affected genes and gene regulation cascades	DNA Res. 2003 Feb 28;10(1):19-25.
Shimamura T, Imoto S, Yamaguchi R, Miyano S	Weighted lasso in graphical Gaussian modeling for large gene network estimation based on microarray data	Genome Inform. 2007;19:142-53.
Shimamura T, Imoto S, Yamaguchi R, Miyano S	Weighted lasso in graphical Gaussian modeling for large gene network estimation based on microarray data	Genome Inform. 2007;19:142-53.
Shimozono S, Miyano S	Complexity of finding alphabet indexing	IEICE Transactions on Information and Systems, E78-D(1), 13-18, 1995
Shimozono S, Shinohara A, Shinohara T, Miyano S, Kuhara S, Arikawa S	Finding alphabet indexing for decision trees over regular patterns: an approach to bioinformatical knowledge acquisition	Proceedings of the Twenty-Sixth Hawaii International Conference on System Sciences, Vol.I. IEEE Computer Society Press, 763-772, 1993
Shinohara A, Miyano S	Teachability in computational learning	New Generation Computing, 8(4), 337-347, 1991
Shinohara A, Shimozono S, Uchida T, Miyano S, Kuhara S, Arikawa S	Running learning systems in parallel for machine discovery from sequences	Genome Informatics, 4, 74-83, 1993
Shoudai T, Lappe M, Miyano S, Shinohara A, Okazaki T, Arikawa S, Uchida T, Shimozono S, Shinohara T, Kuhara S	BONSAI Garden: parallel knowledge discovery system for amino acid sequences	Proc Int Conf Intell Syst Mol Biol. 1995;3:359-66.
Shoudai T, Lappe M, Miyano S, Shinohara A, Okazaki T, Arikawa S, Uchida T, Shimozono S, Shinohara T, Kuhara S	BONSAI Garden: parallel knowledge discovery system for amino acid sequences	Proc Int Conf Intell Syst Mol Biol. 1995;3:359-66.
Shoudai T, Miyano S	A parallel algorithm for the maximal co-hitting set problem	IEICE Transactions on Information and Systems, E76-D(2), 296-298, 1993
Shoudai T, Miyano S	Using maximal independent sets to solve problems in parallel	Theoretical Computer Science, 148(1), 57-65, 1995
Sim KL, Uchida T, Miyano S	ProDDO: a database of disordered proteins from the Protein Data Bank (PDB)	Bioinformatics. 2001 Apr;17(4):379-80.
Standley DM, Yamashita R, Kinjo AR, Toh H, Nakamura H	SeSAW: balancing sequence and structural information in protein functional mapping	Bioinformatics. 2010 May 1;26(9):1258-9. doi: 10.1093/bioinformatics/btq116. Epub 2010 Mar 17.
Sugii M, Okada R, Matsuno H, Miyano S	Performance improvement in protein N-myristoyl classification by BONSAI with insignificant indexing symbol	Genome Inform. 2007;18:277-86.
Sumii E, Bannai H	The extension of ML with hypothetical views for discovery science: formalization and implementation	Journal of Functional and Logic Programming, Vol.2003, Special Issue 1, 2003
Sumii E, Bannai H	VMlambda: a functional calculus for scientific discovery	Proc. 6th International Symposium on Functional and Logic Programming (FLOPS 2002), Lecture Notes in Computer Science, 2441, 290-304, 2002. (Peer-reviewed paper)
Takeda M, Inenaga S, Bannai H, Shinohara A, Arikawa S	Discovering most classificatory patterns for very expressive pattern classes	Proc. 6th International Conference on Discovery Science (DS 2003), Lecture Notes in Computer Science, 2843, 486-493, 2003. (Peer-reviewed paper)
Takei T, Iida A, Nitta K, Tanaka T, Ohnishi Y, Yamada R, Maeda S, Tsunoda T, Takeoka S, Ito K, Honda K, Uchida K, Tsuchiya K, Suzuki Y, Fujioka T, Ujiie T, Nagane Y, Miyano S, Narita I, Gejyo F, Nihei H, Nakamura Y	Association between single-nucleotide polymorphisms in selectin genes and immunoglobulin A nephropathy	Am J Hum Genet. 2002 Mar;70(3):781-6. Epub 2002 Feb 1.
Takei Y, Inoue K, Ogoshi M, Kawahara T, Bannai H, Miyano S	Identification of novel adrenomedullin in mammals: a potent cardiovascular and renal regulator	FEBS Lett. 2004 Jan 2;556(1-3):53-8.
Takei Y, Kawakoshi A, Tsukada T, Yuge S, Ogoshi M, Inoue K, Hyodo S, Bannai H, Miyano S	Contribution of comparative fish studies to general endocrinology: structure and function of some osmoregulatory hormones	J Exp Zool A Comp Exp Biol. 2006 Sep 1;305(9):787-98.
Tamada Y, Bannai H, Imoto S, Katayama T, Kanehisa M, Miyano S	Utilizing evolutionary information and gene expression data for estimating gene networks with bayesian network models	J Bioinform Comput Biol. 2005 Dec;3(6):1295-313.
Tamada Y, Bannai H, Imoto S, Katayama T, Kanehisa M, Miyano S	Utilizing evolutionary information and gene expression data for estimating gene networks with bayesian network models	J Bioinform Comput Biol. 2005 Dec;3(6):1295-313.
Tamada Y, Bannai H, Maruyama O, Miyano S	Foundations of designing computational knowledge discovery processes	Progress in Discovery Science. Lecture Notes in Computer Science, 2281, 459-470, 2002
Tamada Y, Imoto S, Tashiro K, Kuhara S, Miyano S	Identifying drug active pathways from gene networks estimated by gene expression data	Genome Inform. 2005;16(1):182-91.
Tamada Y, Kim S, Bannai H, Imoto S, Tashiro K, Kuhara S, Miyano S	Estimating gene networks from gene expression data by combining Bayesian network model with promoter element detection	Bioinformatics. 2003 Oct;19 Suppl 2:ii227-36.
Tanaka M, Nakazono S, Matsuno H, Tsujimoto H, Kitamura Y, Miyano S	Intelligent system for topic survey in MEDLINE by keyword recommendation and learning text characteristics	Genome Inform Ser Workshop Genome Inform. 2000;11:73-82.
Tasaki S, Nagasaki M, Oyama M, Hata H, Ueno K, Yoshida R, Higuchi T, Sugano S, Miyano S	Modeling and estimation of dynamic EGFR pathway by data assimilation approach using time series proteomic data	Genome Inform. 2006;17(2):226-38.
Tateishi E, Maruyama O, Miyano S	Extracting best consensus motifs from positive and negative examples	Proceedings of the 13th Annual Symposium on Theoretical Aspects of Computer Science. Lecture Notes in Computer Science, 1046, 219-230, 1996
Tateishi E, Miyano S	A greedy strategy for finding motifs from yes-no examples	Pac Symp Biocomput. 1996:599-613.
Termier A, Tamada Y, Imoto S, Washio T, Higuchi T	From closed tree mining towards closed DAG mining	Proc. International Workshop on Data Mining and Statistical Science, 1-7, 2006. (Peer-reviewed conference paper)
Termier A, Tamada Y, Numata K, Imoto S, Washio T, Higuchi T	DIGDAG, a first algorithm to mine closed frequent embedded sub-DAGs	Proc. 5th International Workshop on Mining and Learning with Graphs. CR-ROM, 2007. (MLG2007: Refereed conference). (Peer-reviewed conference paper)
Uchida T, Miyano S	O(log*n) time parallel algorithm for computing bounded degree maximal subgraphs	Journal of Information Processing, 16(1), 16-20, 1993
Uchida T, Shoudai T, Miyano S	Parallel algorithms for refutation tree problem on elementary formal graph systems	IEICE Transactions on Information and Systems, E78-D(2), 99-112, 1995
Uchida T, Shoudai T, Miyano S	Polynomial time algorithm solving the refutation tree problem for formal graph systems	Bulletin of Informatics and Cybernetics, 26(1-2), 55-74, 1994
Usuzaka Si, Sim KL, Tanaka M, Matsuno H, Miyano S	A Machine Learning Approach to Reducing the Work of Experts in Article Selection from Database: A Case Study for Regulatory Relations of S. cerevisiae Genes in MEDLINE	Genome Inform Ser Workshop Genome Inform. 1998;9:91-101.
Washio T, Higuchi T, Imoto S, Tamada Y, Sato K, Motoda H	Graph mining and its application to statistical modeling (in Japanese with English abstract)	Proc. Inst. Statist. Math, 54(2), 315-332, 2006
Yamaguchi A, Nakano K, Miyano S	An approximation algorithm for the minimum common supertree problem	Nordic J. Computing, 4(2), 303-316, 1997
Yamaguchi R, Yamamoto M, Imoto S, Nagasaki M, Yoshida R, Tsuiji K, Ishige A, Asou H, Watanabe K, Miyano S	Identification of activated transcription factors from microarray gene expression data of Kampo medicine-treated mice	Genome Inform. 2007;18:119-29.
Yamaguchi R, Yoshida R, Imoto S, Higuchi T, Miyano S	Finding module-based gene networks with state-space models? Mining high-dimensional and short time-course gene expression data	IEEE Signal Processing Magazine, 24(1), 37-46, 2007
Yasuda T, Bannai H, Onami S, Miyano S, Kitano H	Towards Automatic Construction of Cell-Lineage of C. elegans from Nomarski DIC Microscope Images	Genome Inform Ser Workshop Genome Inform. 1999;10:144-154.
Yoshida R, Higuchi T, Imoto S	A mixed factors model for dimension reduction and extraction of a group structure in gene expression data	Proc IEEE Comput Syst Bioinform Conf. 2004:161-72.
Yoshida R, Higuchi T, Imoto S, Miyano S	ArrayCluster: an analytic tool for clustering, data visualization and module finder on gene expression profiles	Bioinformatics. 2006 Jun 15;22(12):1538-9. Epub 2006 Apr 10.
Yoshida R, Imoto S, Higuchi T	A penalized likelihood estimation on transcriptional module-based clustering	Proc. 1st International Workshop on Data Mining and Bioinformatics, Lecture Note in Comupter Science, 3482, 389-401, 2005. (DMBio2005: Refereed conference)
Yoshida R, Imoto S, Higuchi T	Estimating time-dependent gene networks from time series microarray data by dynamic linear models with Markov switching	Proc IEEE Comput Syst Bioinform Conf. 2005:289-98.
Yoshida R, Numata K, Imoto S, Nagasaki M, Doi A, Ueno K, Miyano S	A statistical framework for genome-wide discovery of biomarker splice variations with GeneChip Human Exon 1.0 ST Arrays	Genome Inform. 2006;17(1):88-99.
Yoshida R, Numata K, Imoto S, Nagasaki M, Doi A, Ueno K, Miyano S	Computational discovery of aberrant splice variations with genome-wide exon expression profiles	Proc. IEEE 7th International Symposium on Bioinformatics & Bioengineering, 715-722, 2007. (IEEE BIBE2007: Refereed conference; Digital Object Identifier 10.1109/BIBE.2007.4375639)
福田賢一郎	BioPAX: パスウェイデータフォーマットの標準化とオントロジー	生物物理 Vol.47 No.3, 179-84, 2007
福田賢一郎	生命科学におけるオントロジーとその利用	人工知能学会誌 Vol.22 No.1, 70-76, 2007
福田賢一郎, 五斗進	バイオデータベースの今:4. バイオ知識の形成と表現	情報処理学会誌 Vol.47 No.3, 233-240, 2006