Y. Katsura, M. Kumagai, T. Kodani, M. Kaneshige, Y. Ando, S. Gunji, Y. Imai, H. Ouchi, K. Tobita, K. Kimura, and K. Tsuda, Data-driven analysis of electron relaxation times in PbTe-type thermoelectric materials, Science and Technology of Advanced Materials, 2019
M. Sumita, R. Tamura, K. Homma, C. Kaneta, and K. Tsuda, Li-ion Conductive Li3PO4-Li3BO3-Li2SO4 Mixture: Prevision through Density Functional Molecular Dynamics and Machine Learning, Bulletin of the Chemical Society of Japan, 2019
Z. Hou, Y. Takagiwa, Y. Shinohara, Y. Xu, and K. Tsuda, Machine-Learning-Assisted Development and Theoretical Consideration for the Al2Fe3Si3 Thermoelectric Material, ACS applied materials & interfaces, 2019
K. Terayama, R. Tamura, Y. Nose, H. Hiramatsu, H. Hosono, Y. Okuno, and K. Tsuda, Efficient construction method for phase diagrams using uncertainty sampling, Physical Review Materials, 3, 3, 033802, 2019
T. Yamashita, S. Kanehira, N. Sato, H. Kino, K. Tsuda, T. Miyake, and T. Oguchi, Crystal Structure Prediction by Bayesian Optimization and Evolutionary Algorithm, Bulletin of the American Physical Society, 2019
D. Das, J. Ito, T. Kadowaki, and K. Tsuda, An interpretable machine learning model for diagnosis of Alzheimer's disease, PeerJ, 7, e6543, 2019
K. Kitai, J. Guo, S. Ju, S. Tanaka, K. Tsuda, J. Shiomi, and R. Tamura, Expanding the horizon of automated metamaterials discovery via quantum annealing, arXiv, 1902.06573, 2019
A. Sakurai, K. Yada, T. Simomura, S. Ju, M. Kashiwagi, H. Okada, T. Nagao, K. Tsuda, and J. Shiomi, Ultranarrow-band wavelength-selective thermal emission with aperiodic multilayered metamaterials designed by Bayesian optimization, ACS central science, 5, 2, 319-326, 2019
T. Dieb, S. Ju, J. Shiomi, and K. Tsuda, Monte Carlo tree search for materials design and discovery, MRS Communications, 1-5, 2019
M. Araki, H. Iwata, B. Ma, A. Fujita, K. Terayama, Y. Sagae, F. Ono, K. Tsuda, N. Kamiya, and Y. Okuno, Improving the Accuracy of Protein‐Ligand Binding Mode Prediction Using a Molecular Dynamics‐Based Pocket Generation Approach, Journal of computational chemistry, 39, 32, 2679-2689, 2018
R. Morioka, K. Nansai, and K. Tsuda, Role of linkage structures in supply chain for managing greenhouse gas emissions, Journal of Economic Structures, 7, 1, 7, 2018
N. Yoshikawa, K. Terayama, M. Sumita, T. Homma, K. Oono, and K. Tsuda, Population-based de novo molecule generation, using grammatical evolution, Chemistry Letters, 47, 11, 1431-1434, 2018
M. Sugiyama, H. Nakahara and K. Tsuda, Legendre Decomposition for Tensors, Advances in Neural Information Processing Systems 31, 2018
S. Kiyohara, T. Miyata, K. Tsuda and T. Mizoguchi, Data-driven approach for the prediction and interpretation of core-electron loss spectroscopy, Scientific Reports, 8, 13548, 2018.
M. Sumita, X. Yang, S. Ishihara, R. Tamura, and K. Tsuda, Hunting for Organic Molecules with Artificial Intelligence: Molecules Optimized for Desired Excitation Energies, ACS Central Science, 4, 1126-1133, 2018.
Y. Saito, M. Oikawa, H. Nakazawa, T. Niide, T. Kameda, K. Tsuda, M. Umetsu, Machine-Learning-Guided Mutagenesis for Directed Evolution of Fluorescent Proteins, ACS Synthetic Biology, 7, 2014-2022, 2018.
K. Shiba, R. Tamura, T. Sugiyama, Y. Kameyama, K. Koda, E. Sakon, K. Minami, H.T. Ngo, G. Imamura, K. Tsuda and G. Yoshikawa, Functional Nanoparticles-Coated Nanomechanical Sensor Arrays for Machine Learning-Based Quantitative Odor Analysis, ACS Sensors, 3, 8, 1592-1600, 2018.
S. Denzumi, J. Kawahara, K. Tsuda, H. Arimura, S. Minato, K. Sadakane, DenseZDD: A Compact and Fast Index for Families of Sets, Algorithms, 11, 128, 2018
K. Terayama, T. Yamashita, T. Oguchi and K. Tsuda, Fine-grained optimization method for crystal structure prediction, npj Computational Materials, 4, 32, 2018.
R. Morioka, K. Nansai and K. Tsuda, Role of linkage structures in supply chain for managing greenhouse gas emissions, Journal of Economic Structures, 7, 7, 2018.
K. Yoshizoe, A. Terada and K. Tsuda, MP-LAMP: Parallel Detection of Statistically Significant Multi-Loci Markers on Cloud Platforms, Bioinformatics, 34, 3047-3049, 2018.
T.M. Dieb, Z. Hou and K. Tsuda, Structure prediction of boron-doped graphene by machine learning, The Journal of Chemical Physics, 148, 241716 (2018)
K. Terayama, H. Iwata, M. Araki, Y. Okuno and K. Tsuda, Machine Learning Accelerates MD-based Binding-Pose Prediction between Ligands and Proteins, Bioinformatics, 34, 770-778, 2018.
R. Tamura and K. Hukushima, Bayesian optimization for computationally extensive probability distributions, PLoS ONE, 13, e0193785, 2018.
T. M. Dieb, K. Tsuda, Machine Learning-Based Experimental Design in Materials Science. In: Tanaka I. (eds) Nanoinformatics. Springer, Singapore,pp 65-74
T. Yamashita, N. Sato, H. Kino, T. Miyake, K. Tsuda, and T. Oguchi, Crystal structure prediction accelerated by Bayesian optimization, Physical Review Materials, 2, 013803, 2018.
X. Yang, J. Zhang, K. Yoshizoe, K. Terayama and K. Tsuda, ChemTS: an efficient python library for de novo molecular generation, Science and Technology of Advanced Materials, 18, 972-976, 2017.
H. Oda, S. Kiyohara, K. Tsuda and T. Mizoguchi, Transfer Learning to Accelerate Interface Structure Searches, Journal of the Physical Society of Japan, 86, 123601, 2017.
X. Yang, K. Yoshizoe, A. Taneda and K. Tsuda, RNA inverse folding using Monte Carlo tree search, BMC Bioinformatics, 18:468, 2017.
T.L. Pham, H. Kino, K. Terakura, T. Miyake, K. Tsuda, I. Takigawa and H.C. Dam, Machine learning reveals orbital interaction in materials, Science and Technology of Advanced Materials, 18, 756-765, 2017.
T.M. Dieb, S. Ju, K. Yoshizoe, Z. Hou, J. Shiomi, K. Tsuda, MDTS: automatic complex materials design using Monte Carlo tree search, Science and Technology of Advanced Materials, 18, 498-503, 2017.
S. Ju, T. Shiga, L. Feng, Z. Hou, K. Tsuda, and J. Shiomi, Designing Nanostructures for Phonon Transport via Bayesian Optimization, Physical Review X, 7, 021024, 2017.
竹内一郎, 中川和也, 津田宏治: 高次交互作用モデリングのための機械学習アルゴリズム, 日本ロボット学会誌, 3, 215-220, 2017.
S. Suzumura, K. Nakagawa, Y. Umezu, K. Tsuda, I. Takeuchi, Selective Inference for Sparse High-Order Interaction Models, Proceedings of the 34th International Conference on Machine Learning (ICML 2017), pp. 3338-3347, 2017.
M. Sugiyama, H. Nakahara, K. Tsuda. Tensor Balancing on Statistical Manifold, Proceedings of the 34th International Conference on Machine Learning (ICML 2017), pp. 3270-3279, 2017.
H. Arai, J. Sakuma and K. Tsuda, Quantifying Genomic Privacy in Genetic Test Results, 3rd International Workshop on Genome Privacy and Security (Genopri 2016), 2016.
Diptesh Das, Atsuhiro Takasu, "Trajectory Data Mining using Deep Neural Network" in proc. of the 6th International Workshop on Mobile Entity Localization, Tracking and Analysis (ACM SIGSPATIAL Workshop - MELT 2016), San Francisco Bay Area, CA, USA, October 31- November 03, 2016
T. M. Dieb, K. Tsuda, DAMAS: An Annotation Support Tool for Materials Information. In Proceeding of the 5th Asian Materials Data Symposium (AMDS2016), pp. 242-245, Hanoi, Vietnam. Oct.30 – Nov. 2, 2016.
D. A. duVerle, S. Yotsukura, S. Nomura, H. Aburatani and K. Tsuda, CellTree: an R/bioconductor package to infer the hierarchical structure of cell populations from single-cell RNA-seq data, BMC Bioinformatics, 17, 363, 2016.
M. Shiga, K. Tatsumi, S. Muto, K. Tsuda, Y. Yamamoto, T. Mori and T. Tanji, Sparse Modeling of EELS and EDX Spectral Imaging Data by Nonnegative Matrix Factorization, Ultramicroscopy, 170, 43-59, 2016.
K. Terayama, H. Habe and M. Sakagami, Multiple fish tracking with an NACA airfoil model for collective behavior analysis, IPSJ Transactions on Computer Vision and Applications, 8(4), 1-7, 2016.
M. Shiga, S. Muto, K. Tatsumi and K. Tsuda, Matrix Factorization for Automatic Chemical Mapping from Electron Microscopic Spectral Imaging Datasets, Transactions of the Materials Research Society of Japan, 41, 333-336, 2016.
A. Terada, R. Yamada, K. Tsuda and J. Sese, LAMPLINK: detection of statistically significant SNP combinations from GWAS data, Bioinformatics, 32 (22), 3513-3515, 2016.
I. Takigawa, K. Shimizu, K. Tsuda and S. Takakusagi, Machine-learning prediction of d-band center for metals and bimetals, RSC Advances, 6, 52587-52595, 2016.
K. Nakagawa, S. Suzumura, M. Karasuyama, K. Tsuda and I. Takeuchi, Safe Pattern Pruning: An Efficient Approach for Predictive Pattern Mining, 22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), pages 1785-1794, 2016
Sinharay, Arijit, Aniruddha Sinha, Diptesh Das, and Debatri Chatterjee. "SELECTION OF ELECTROENCEPHALOGRAPHY (EEG) CHANNELS VALID FOR DETERMINING COGNITIVE LOAD OF A SUBJECT." U.S. Patent 20,160,128,593, issued May 12, 2016.
M. Sugiyama, H. Nakahara and K. Tsuda, Information Decomposition on Structured Space, 2016 IEEE International Symposium on Information Theory, pages 575-579, 2016.
T. Ueno, T.D. Rhone, Z. Hou, T. Mizoguchi and K. Tsuda, COMBO: An Efficient Bayesian Optimization Library for Materials Science, Materials Discovery, 4, 18-21, 2016.
S. Kiyohara, H. Oda, K. Tsuda and T. Mizoguchi, Acceleration of stable interface structure searching using a kriging approach, Japanese Journal of Applied Physics, 55, 045502, 2016.
A. Terada, D.A. duVerle and K. Tsuda, Significant Pattern Mining with Confounding Variables, 20th Pacific Asia Conference on Knowledge Discovery and Data Mining (PAKDD), pp. 277-289, 2016.
K. Shimizu, K. Nuida, H. Arai, S. Mitsunari, N. Attrapadung, M. Hamada, K. Tsuda, T. Hirokawa, J. Sakuma, G. Hanaoka, K. Asai, Privacy-preserving search for chemical compound databases, BMC Bioinformatics, 16(Suppl 18):S6, 2015.
Identification of people using multiple skeleton recording devices, K Chakravarty, A Sinha, D Das, R Banerjee, A Konar, S Dutta, US Patent 9,208,376 (GRANTED) https://www.google.com/patents/US9208376
J. Sese, A. Terada, Y. Saito, K. Tsuda. Statistically significant subgraphs for genome-wide association study, JMLR: Workshop and Conference Proceedings, 47:29–36, 2015.
Dynamir: Optical Manipulations Using Dynamic Mirror Brushes
Berthaut Florent, Sahoo Deepak, Ranjan, McIntosh Jess, Das Diptesh, Subramanian Sriram
ITS '15 Proceedings of the 2015 International Conference on Interactive Tabletops & Surfaces, 55-58,ACM New York, NY, USA ©2015
Method and system for implementation of an interactive television application, D Das, A Ghose, P Sinha, P Biswas, US Patent 9,185,462 (GRANTED) https://www.google.com/patents/US9185462
K. Terayama, K. Hongo, H. Habe and M. Sakagami, Appearance-based Multiple Fish Tracking for Collective Motion Analysis, The Third IAPR Asian Conference on Pattern Recognition (ACPR 2015), Kuala Lumpur, 2015.
A. Seko, A. Togo, H. Hayashi, K. Tsuda, L. Chaput, and I. Tanaka, Prediction of low-thermal-conductivity compounds with first-principles anharmonic lattice-dynamics calculations and Bayesian optimization, Physical Review Letters, 115, 205901, 2015.
K. Terayama and M. Sakagami, Measurement of Velocity Fields of Schools of Sardines and Existence of Averaged Tori, The First International Symposium on Swarm Behavior and Bio-Inspired Robotics (SWARM 2015), pp.324-325, Kyoto, 2015.
武藤俊介, 志賀元紀, 巽一厳, 津田宏治: ナノ電子顕微分光における情報処理技法の応用, セラミックス, 50(7), pp.527-530, 2015.
Y. Baba, H. Kashima, Y. Nohara, E. Kai, P. Ghosh, R. Islam, A. Ahmed, M. Kuroda, S. Inoue, T. Hiramatsu, M. Kimura, S. Shimizu, K. Kobayashi, K. Tsuda, M. Sugiyama, M. Blondel, N. Ueda, M. Kitsuregawa, N. Nakashima. Predictive Approaches for Low-cost Preventive Medicine Program in Developing Countries. Proceedings of the 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), pp. 1681-1690, 2015.
K. Terayama, H. Hioki and M. Sakagami, A measurement method for speed distribution of collective motion with optical flow and its applications to school of fish, International Journal of Semantic Computing, Vol.9, No.2, pp.143-168, 2015.
K. Terayama and H. Hioki, A Practical Classifier for Photographs and Non-Photographic Images Based on Local Visual Features, The 14th IAPR Conference on Machine Vision Applications (MVA 2015), pp.307-311, Tokyo, 2015.
D. A. duVerle, S. Kawasaki, Y. Yamada, J. Sakuma and K. Tsuda, Privacy-Preserving Statistical Analysis by Exact Logistic Regression, 2nd International Workshop on Genome Privacy and Security (Genopri’15), pages 7-16, 2015.
T. Toda, K. Tsuda. BDD Construction for All Solutions SAT and Efficient Caching Mechanism. 30th Annual ACM Symposium on Applied Computing, pp. 1880-1886, 2015
T. Toda, S. Takeuchi, K. Tsuda, S. Minato. Superset Generation on Decision Diagrams. 9th International Workshop on Algorithms and Computation (WALCOM 2015), pp. 317-322, 2015.
Y. Nohara, E. Kai, P. P. Ghosh, R. Islam, A. Ahmed, M. Kuroda, S. Inoue, T. Hiramatsu, M. Kimura, S. Shimizu, K. Kobayashi, Y. Baba, H. Kashima, K. Tsuda, M. Sugiyama, M. Blondel, N. Ueda, M. Kitsuregawa, and N. Nakashima: Health Checkup and Telemedical Intervention Program for Preventive Medicine in Developing Countries: Verification Study, J. Med. Internet Res., vol. 17, no. 1, p. e2, 2015.
Tsukui T, Nagano N, Umemura M, Kumagai T, Terai G, Machida M, Asai K. Ustiloxins, fungal cyclic peptides, are ribosomally synthesized in Ustilaginoidea virens. Bioinformatics oxfordjournals Apr 1;31(7):pp981-5. 2016.
K. Terayama, H. Hioki and M. Sakagami, A Measurement Method for Speed Distribution of Collective Motion with Optical Flow and its Application to Estimation of Rotation Curve, IEEE International Symposium on Multimedia (ISM 2014), pp.32-39, Taichung, 2014.
A. Sinha, D. Chatterjee, D. Das and A. Sinharay, "Analysis of Cognitive Load -- Importance of EEG Channel Selection for Low Resolution Commercial EEG Devices," Bioinformatics and Bioengineering (BIBE), 2014 IEEE International Conference on, Boca Raton, FL, 2014, pp. 341-348.
K. Moridomi, K. Hatano, E. Takimoto and K. Tsuda. Online matrix prediction for sparse loss matrices. 6th Asian Conference on Machine Learning, pp. 250-265, 2014.
津田宏治: ビッグデータからの科学的発見をもたらす統計手法, ファルマシア, 50(10), 993-997, 2014.
R. Das, D. Chatterjee, D. Das, A. Sinharay and A. Sinha, "Cognitive load measurement - A methodology to compare low cost commercial EEG devices," Advances in Computing, Communications and Informatics (ICACCI, 2014 International Conference on, New Delhi, 2014, pp. 1188-1194.
H. Sasakawa, H. Harada, D. duVerle, H. Arimura, K. Tsuda and J. Sakuma. Oblivious Evaluation of Non-deterministic Finite Automata with Application to Privacy-Preserving Virus Genome Detection, Proceedings of the 13th ACM Workshop on Privacy in the Electronic Society, pages 21-30, 2014.
A. Sinha, D. Das, K. Chakravarty, A. Konar and S. Dutta, Kinect based people identification system using fusion of clustering and classification, " Computer Vision Theory and Applications (VISAPP), 2014 International Conference on, Lisbon, Portugal, 2014, pp. 171-179.
A. Seko, T. Maekawa, K. Tsuda, and I. Tanaka: Machine learning with systematic density-functional theory calculations: Application to melting temperatures of single- and binary-component solids, Physical Review B, 89:054303, 2014.
瀬々潤, 寺田愛花, 津田宏治: 網羅的トランスオミクスにおけるビッグデータの解析, 実験医学 32(8): 1210 -1214, 2014
S. Minato, T. Uno, K. Tsuda, A. Terada, J. Sese, A Fast Method of Statistical Assessment for Combinatorial Hypotheses Based on Frequent Itemset Enumeration, ECMLPKDD, pages 422-436, 2014.
S. Denzumi, J. Kawahara, K. Tsuda, H. Arimura, S. Minato and K. Sadakane. DenseZDD: A Compact and Fast Index for Families of Sets, Symposium on Experimental Algorithms, pages 187-198, 2014.
津田宏治,寺田愛花,瀬々潤: 生命科学データからの組み合わせ発見問題, 電子情報通信学会誌, 97, 5, pp.359-363,2014.
森岡涼子, 津田宏治: 情報幾何的分解に基づく地方産業連関表の将来推計, 京都大学数理解析研究所講究録, 1916:85-102 (2014)
T. Inoue, K. Takano, T. Watanabe, J. Kawahara, R. Yoshinaka, A. Kishimoto, K. Tsuda, S. Minato and Y. Hayashi. Distribution Loss Minimization with Guaranteed Error Bound. IEEE Transactions on Smart Grid, 5(1):102-111, 2014.
D. Das, D. Chatterjee and A. Sinha, "Unsupervised approach for measurement of cognitive load using EEG signals," Bioinformatics and Bioengineering (BIBE), 2013 IEEE 13th International Conference on, Chania, 2013, pp. 1-6.
K. Terayama, H. Tsuiki, A Stream Calculus of Bottomed Sequences for Real Number Computation, Electronic Notes in Theoretical Computer Science, Vol.298, pp.383-402, 2013.
D. Das, A. Sinha, K. Chakravarty and A. Konar, "Stabilization of cluster centers over fuzziness control parameter in component-wise Fuzzy c-Means clustering," Fuzzy Systems (FUZZ), 2013 IEEE International Conference on, Hyderabad, 2013, pp. 1-8.
K. Chakravarty, D. Das, A. Sinha and A. Konar, "Feature selection by Differential Evolution algorithm - A case study in personnel identification," 2013 IEEE Congress on Evolutionary Computation, Cancun, 2013, pp. 892-899.
A. Terada, M. Okada-Hatakeyama, K. Tsuda and J. Sese. Statistical significance of combinatorial regulations. Proceedings of the National Academy of Sciences of the United States of America, 110(32):12996-13001, 2013.
C. Xiao, J. Qin, W. Wang, Y. Ishikawa, K. Tsuda and K. Sadakane: Efficient Error-tolerant Query Autocompletion, Proceedings of the VLDB Endowment, 6(6):373-384, 2013.
H. Saigo, H. Kashima and K. Tsuda: Fast Iterative Mining Using Sparsity-Inducing Loss Functions, IEICE Transactions on Information and Systems, E96-D(8):1766-1773, 2013.
I. Takigawa, K. Tsuda and H. Mamitsuka: An In Silico Model for Interpreting Polypharmacological Relationships in Drug-Target Networks, Methods Mol. Biol., 993:67-80, 2013.
K. Tsuda and G.Georgii: Dense Module Enumeration in Biological Networks, Methods Mol. Biol.,939:1-8, 2013
A. Terada, K. Tsuda, and J. Sese. Fast Westfall-Young Permutation Procedure for Combinatorial Regulation Discovery, IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pages 153-158, 2013.
S. Denzumi, K. Tsuda, H. Arimura and S. Minato. Compact Complete Inverted Files for Texts and Directed Acyclic Graphs Based on Sequence Binary Decision Diagrams, Prague Stringology Conference, pages 157-167, 2013.
D. duVerle, I. Takeuchi, Y. Murakami-Tonami, K. Kadomatsu and K. Tsuda. Discovering Combinatorial Interactions in Survival Data. Bioinformatics, 29(23):3053-3059, 2013.
Avik Ghose, Chirabrata Bhaumik, Diptesh Das, and Amit Kumar Agrawal. 2012. Mobile healthcare infrastructure for home and small clinic. In Proceedings of the 2nd ACM international workshop on Pervasive Wireless Healthcare (MobileHealth '12). ACM, New York, NY, USA, 15-20.
C Bhaumik, A Agrawal, S Adak, A Ghose, D Das. Sensor observation service based medical instrument integration, SMART' 2012.
J. Ito, Y. Tabei, K. Shimizu, K. Tomii and K. Tsuda: PDB-scale Analysis of Known and Putative Ligand-binding Sites with Structural Sketches, Proteins, 80:747-763, 2012.
J. Ito, Y. Tabei, K. Shimizu, K. Tsuda and K. Tomii: PoSSuM: a Database of Similar Protein-Ligand Binding and Putative Pockets, Nucleic Acids Research, 40(D1):D541–D548, 2012.
D. Das, P. Sinha, A. Ghose and C. Bhaumik, "An interactive system using digital broadcasting and Quick Response code," Consumer Electronics (ISCE), 2011 IEEE 15th International Symposium on, Singapore, 2011, pp. 397-400.
C. Bhaumik P.Sinha, A. Ghose, D. Das, Crowd sourced tagging of objects tracked on TV, International Broadcasting Conference 2011, Amsterdam
Y. Tabei and K. Tsuda. Kernel-based Similarity Search in Massive Graph Databases with Wavelet Trees. 11th SIAM International Conference on Data Mining (SDM2011), pages 154-163, 2011.
Y. Tabei and K. Tsuda. SketchSort: Fast All Pairs Similarity Search for Large Databases of Molecular Fingerprints. Molecular Informatics, 30(9):801-807, 2011.
K. Shimizu and K. Tsuda. SlideSort: All Pairs Similarity Search for Short Reads. Bioinformatics, 27(4):464-470, 2011.
E. Georgii, K. Tsuda and B. Schoelkopf: Multi-way Set Enumeration in Weight Tensors, Machine Learning, 82(2):123-155, 2011.
I. Takigawa, K. Tsuda and H. Mamitsuka. Mining Significant Substructure Pairs for Interpreting Polypharmacology in Drug-Target Network. PLoS One, 6(2):e16999, 2011.
T. Kam-Thong, D. Czamara, K. Tsuda, K. Borgwardt, C. M. Lewis, A. Erhardt-Lehmann, B. Hemmer, P. Rieckmann, M. Daake, F. Weber, C. Wolf, A. Ziegler, B. Putz, F. Holsboer, B. Scholkopf and B. Muller-Myhsok. EPIBLASTER-Fast exhaustive two-locus epistasis detection strategy using graphical processing units. European Journal of Human Genetics, 19:465-471, 2011.
M. Kayano, I. Takigawa, M. Shiga, K. Tsuda and H. Mamitsuka. ROS-DET: Robust Detector of Switching Mechanisms in Gene Expression. Nucleic Acids Research, 39(11), e74, 2011.
Y. Tabei, D. Okanohara, S. Hirose and K. Tsuda. LGM: Mining Frequent Subgraphs from Linear Graphs.15th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD2011), pages 26-37, 2011.
H. Saigo, M. Hattori, H. Kashima and K. Tsuda: Reaction Graph Kernels Predict EC Numbers of Unknown Enzymatic Reactions in Plant Secondary Metabolism, BMC Bioinformatics, 11 (suppl.1):S31, 2010.
Y. Tabei, T. Uno, M. Sugiyama and K. Tsuda: Single versus Multiple Sorting in All Pairs Similarity Search, Proceedings of the 2nd Asian Conference on Machine Learning, pages 155-170, 2010.
M. Kayano, I. Takigawa, M. Shiga, K. Tsuda and M. Mamitsuka. On the Performance of Methods for Finding a Switching Mechanism in Gene Expression. Genome Informatics, 24:69-83, 2010
H. Kashima, S. Oyama, Y. Yamanishi and K. Tsuda: Cartesian Kernel: An Efficient Alternative to the Pairwise Kernel, IEICE Transaction on Information and Systems, E93-D:2672-2679, 2010.
M. Kayano, I. Takigawa, M. Shiga, K. Tsuda and H. Mamitsuka: Efficiently Finding Genome-wide Three-way Interactions from Transcript- and Genotype-Data, Bioinformatics, 25:2735-2743, 2009.
H. Kashima, Y. Yamanishi, T. Kato, M. Sugiyama and K. Tsuda: Simultaneous Inference of Biological Networks of Multiple Species from Genome-wide Data and Evolutionary Information: A Semi-supervised Approach. Bioinformatics, 25:2962-2968, 2009.
E. Georgii, S. Dietmann, T. Uno, P. Pagel and K. Tsuda: Enumeration of Condition-Dependent Dense Modules in Protein Interaction Networks. Bioinformatics, 25:933-940, 2009.
S. Dietmann, E. Georgii, A. Antonov, K. Tsuda and H.W. Mewes. The DICS repository: module-assisted analysis of disease-related gene lists. Bioinformatics, 25:830-831, 2009.
H. Saigo, S. Nowozin, T. Kadowaki, T. Kudo, and K. Tsuda. gBoost: A mathematical programming approach to graph classification and regression. Machine Learning, 75:69-89, 2009.
H. Shin, K. Tsuda, and B. Schoelkopf. Protein functional class prediction with a combined graph. Expert Systems with Applications, 36:3284-3292, 2009.
Y. Kawahara, K. Nagano, K. Tsuda and J. Bilmes: Submodularity Cuts and Applications, Advances in Neural Information Processing Systems 22, pages 916-924, 2009.
E. Georgii, K. Tsuda and B. Schoelkopf: Multi-Way Set Enumeration in Real-Valued Tensors, Proceedings of the KDD 2009 Workshop on Data Mining using Matrices and Tensors (DMMT09), pages 32-41, 2009
S. Chiappa, H. Saigo and K. Tsuda: A Bayesian Approach to Graph Regression with Relevant Subgraph Selection. In Proceedings of 2009 SIAM International Conference on Data Mining (SDM), pages 295-304, 2009.
H. Kashima, T. Kato, Y. Yamanishi, M. Sugiyama, K. Tsuda. Link Propagation: A Fast Semi-supervised Learning Algorithm for Link Prediction. In Proceedings of 2009 SIAM International Conference on Data Mining (SDM), pages 1099-1110, 2009.
H. Kashima, S. Oyama, Y. Yamanishi and K. Tsuda. On Pairwise Kernels: An Efficient Alternative and Generalization Analysis. In Proceedings of the 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), pages 1030-1037, 2009.
K. Tsuda. Machine Learning with Quantum Relative Entropy, Journal of Physics: Conference Series, 143, 012021, 2009.
S. Nowozin and K. Tsuda. Frequent Subgraph Retrieval in Geometric Graph Databases. In Proceedings of the 8th IEEE International Conference on Data Mining (ICDM2008), pages 953-958, 2008.
H. Saigo and K. Tsuda. Iterative Subgraph Mining for Principal Component Analysis. In Proceedings of the 8th IEEE International Conference on Data Mining (ICDM2008), pages 1007-1012, 2008
H. Saigo, N. Kraemer, and K. Tsuda. Partial least squares regression for graph mining. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD2008), pages 578-586, 2008.
K. Tsuda and K. Kurihara. Graph mining with variational Dirichlet process mixture models. In 2008 SIAM Conference on Data Mining, pages 432–442, 2008.
K. Tanaka and K. Tsuda. A quantum-statistical-mechanical extension of Gaussian mixture model. Journal of Physics: Conference Series, 95, 012023, 2008
F. Steinke, M. Seeger, and K. Tsuda. Experimental design for efficient identification of gene regulatory networks using sparse Bayesian models. BMC Systems Biology, 1:51, 2007.
H. Saigo, T. Uno, and K. Tsuda. Mining complex genotypic features for predicting HIV-1 drug resistance. Bioinformatics, 23(18):2455–2462, 2007.
K. Tsuda. Entire regularization paths for graph data. In Proceedings of the 24th International Conference on Machine Learning, pages 919–926, 2007.
S. Nowozin, G. Bakir, and K. Tsuda. Discriminative subsequence mining for action classification. In Proceedings of the 11th IEEE International Conference on Computer Vision (ICCV 2007). IEEE Computer Society, 2007.
S. Nowozin, K. Tsuda, T. Uno, T. Kudo, and G. Bakir. Weighted substructure mining for image analysis. In Proceedings of the 2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2007). IEEE Computer Society, 2007.
T. Ide and K. Tsuda. Change-point detection using Krylov subspace learning. In SIAM Conference on Data Mining (SDM), pages 515–520, 2007.
M. Hamada, K. Tsuda, T. Kudo, T. Kin, and K. Asai. Mining frequent stem patterns from unaligned RNA sequences. Bioinformatics, 22:2480–2487, 2006.
Y. Tabei, K. Tsuda, T. Kin, and K. Asai. SCARNA: fast and accurate structural alignment of RNA sequences by matching fixed-length stem fragments. Bioinformatics, 22:1723–1729, 2006.
T. Kato, Y. Murata, K. Miura, K. Asai, P.B. Horton, K. Tsuda, and W. Fujibuchi. Network-based de-noising improves prediction from microarray data. BMC Bioinformatics, 7(Suppl. 1):S4, 2006.
H. Saigo, T. Kadowaki, and K. Tsuda. A linear programming approach for molecular QSAR analysis. In Proceedings of the International Workshop on Mining and Learning with Graphs (MLG), pages 85–96, 2006.
K. Tsuda and T. Kudo. Clustering graphs by weighted substructure mining. In W.W. Cohen and A. Moore, editors, Proceedings of the 23rd International Conference on Machine Learning (ICML), pages 953–960. ACM Press, 2006.
K. Tsuda, H.J. Shin, and B. Schoelkopf. Fast protein classification with multiple networks. Bioinformatics, 21(Suppl. 2):ii59–ii65, 2005.
K. Tsuda, G. Raetsch, and M.K. Warmuth. Matrix exponentiated gradient updates for online learning and Bregman projection. Journal of Machine Learning Research, 6:995–1018, 2005.
K. Tsuda and G. Raetsch. Image reconstruction by linear programming. IEEE Trans. on Image Processing, 14(6):737–744, 2005.
T. Kato, K. Tsuda, and K. Asai. Selective integration of multiple biological data for supervised network inference. Bioinformatics, 21(10):2488–2495, 2005.
K. Tsuda. Propagating distributions on a hypergraph by dual information regularization. In L. De Raedt and S. Wrobel, editors, Proceedings of the 22nd International Conference on Machine Learning, pages 921–928. ACM, 2005.
K. Tsuda, G. Raetsch, and M.K. Warmuth. Matrix exponentiated gradient updates for online learning and Bregman projection. In L.K. Saul, Y.Weiss, and L. Bottou, editors, Advances in Neural Information Processing Systems 17, pages 1425–1432. MIT Press, 2005.
K. Tsuda, S. Akaho, M. Kawanabe, and K.R. Mueller. Asymptotic properties of the Fisher kernel. Neural Computation, 16(1):115–137, 2004.
K. Tsuda and W.S. Noble. Learning kernels from biological networks by maximizing entropy. Bioinformatics, 20(Suppl. 1):i326–i333, 2004.
K. Tsuda, S. Uda, T. Kin, and K. Asai. Minimizing the cross validation error to mix kernel matrices of heterogeneous biological data. Neural Processing Letters, 19:63–72, 2004.
T. Kato, K. Tsuda, K. Tomii, and K. Asai. A new variational framework for rigid-body alignment. In A. Fred, T. Caelli, R.P.W. Duin, A. Campilho, and D. de Ridder, editors, Proceedings of Joint IAPR International Workshops on Syntactical and Structural Pattern Recognition (SSPR 2004) and Statistical Pattern Recognition (SPR 2004), pages 171–179. Springer Verlag, 2004.
K. Tsuda and G. Raetsch. Image reconstruction by linear programming. In S. Thrun, L. Saul, and B. Schoelkopf, editors, Advances in Neural Information Processing Systems 16, pages 57–64. MIT Press, 2004.
G.H. Bakir, A. Zien, and K. Tsuda. Learning to find graph pre-images. In C.E. Rasmussen, H.H. Buelthoff, M.A. Giese, and B. Schoelkopf, editors, Pattern Recognition, Proceedings of the 26th DAGM Symposium, volume LNCS 3175, pages 253–261. Springer Verlag, 2004.
H.J. Shin, K. Tsuda, and B. Schoelkopf. Protein functional class prediction with a combined graph. In Proc. of the Korean Data Mining Conference, pages 200–219, 2004.
K. Tsuda, S. Akaho, and K. Asai. The em algorithm for kernel matrix completion with auxiliary data. Journal of Machine Learning Research, 4:67–81, May 2003.
H. Kashima, K. Tsuda, and A. Inokuchi. Marginalized kernels between labeled graphs. In T. Faucett and N. Mishra, editors, Proceedings of the 20th International Conference on Machine Learning, pages 321–328, Menlo Park, CA, AAAI Press, 2003.
K. Tsuda, M. Kawanabe, and K.-R. Mueller. Clustering with the Fisher score. In S. Becker, S. Thrun, and K. Obermayer, editors, Advances in Neural Information Processing Systems 15, pages 729–736. MIT Press, 2003.
M. Arita, K. Tsuda, and K. Asai. Modeling splicing sites with pairwise correlations. Bioinformatics, 18(Suppl. 2):S27–S34, 2002.
K. Tsuda, T. Kin, and K. Asai. Marginalized kernels for biological sequences. Bioinformatics, 18(Suppl. 1):S268–S275, 2002.
K. Tsuda, M. Kawanabe, G. Raetsch, S. Sonnenburg, and K.-R. Mueller. A new discriminative kernel from probabilistic models. Neural Computation, 14(10):2397–2414, 2002.
K. Tsuda, M. Sugiyama, and K.-R. Mueller. Subspace information criterion for non-quadratic regularizers – model selection for sparse regressors. IEEE Trans. Neural Networks, 13(1):70–80, 2002.
T. Kin, K. Tsuda, and K. Asai. Marginalized kernels for RNA sequence data analysis. In R.H. Lathtop, K. Nakai, S. Miyano, T. Takagi, and M. Kanehisa, editors, Genome Informatics 2002, pages 112–122. Universal Academic Press, 2002.
K. Tsuda and M. Kawanabe. The leave-one-out kernel. In J.R. Dorronsoro, editor, Artificial Neural Networks – ICANN 2002, LNCS 2415, pages 727–732. Springer Verlag, 2002
K. Tsuda, M. Kawanabe, G. Raetsch, S. Sonnenburg, and K.-R. Mueller. A new discriminative kernel from probabilistic models. In T.G. Dietterich, S. Becker, and Z. Ghahramani, editors, Advances in Neural Information Processing Systems 14, pages 977–984. MIT Press, 2002.
津田宏治, 杉山将, Klaus-Robert Mueller: モデル選択基準 SICのスパース回帰分析への適用, 信学論, J85-D-II, 5, pp. 766-775, 2002.
S. Muraki, T. Nakai, Y. Kita, and K. Tsuda. An attempt for coloring multichannel MR imaging data. IEEE Trans. Visualization and Computer Graphics, 7(3):265–274, 2001.
K.-R. Mueller, S. Mika, G. Raetsch, K. Tsuda, and B. Schoelkopf. An introduction to kernel-based learning algorithms. IEEE Trans. Neural Networks, 12(2):181–201, 2001.
K. Tsuda, G. Raetsch, S. Mika, and K.-R. Mueller. Learning to predict the leave-one-out error of kernel based classifiers. In G. Dorffner, H. Bischof, and K. Hornik, editors, Artifical Neural Networks – ICANN 2001, LNCS 2130, pages 331–338. Springer Verlag, 2001.
V. Roth and K. Tsuda. Pairwise coupling for machine recognition of handprinted japanese characters. In IEEE Conference on Computer Vision and Pattern Recognition, volume 1, pages 1120–1125, 2001.
K. Tsuda and S. Akaho. Large margin classifier via semiparametric inference. In Proceedings of the International Joint Conference on Neural Networks (IJCNN 2000), volume 2, pages 23–29. IEEE CS Press, 2000.
K. Tsuda. Subspace classifier in the Hilbert space. Pattern Recognition Letters, 20:513–519, 1999.
K. Tsuda. Optimal hyperplane classifier based on entropy number bound. In ICANN 99: Ninth International Conference on Artifical Neural Networks, pages 419–424. IEE, 1999.
K. Tsuda. Subspace classifier in reproducing kernel hilbert space. In International Joint Conference on Neural Networks (IJCNN’99) Proceedings, 1999.
K. Tsuda. Support vector classifier with asymmetric kernel functions. In M. Verleysen, editor, Proceedings of ESANN’99 - European Symposium of Artifical Neural Networks, pages 183–188. D Facto, 1999.
津田宏治: “ヒルベルト空間における部分空間法”, 信学論, J82-D-II, 4, pp. 592-599, 1999.
H. Yoshiuchi, K. Tsuda, S. Fukushima, and M. Minoh. Pattern recognition method for metric space by four points embedding. In Image and Vision Computing New Zealand (IVCNZ’98), pages 186–191, 1998.
K. Tsuda and M. Minoh. A nonparametric density model for classification in a high dimensional space. In Proc. 4th Int. Conf. Document Analysis and Recognition, pages 1082–1086, 1997.
K. Tsuda, S. Senda, M. Minoh, and K. Ikeda. Sequential fuzzy cluster extraction and its robustness against noise. Systems and Computers in Japan, 28(6):10–17, 1997.
津田宏治, 仙田修司, 美濃導彦, 池田克夫: ``逐次的ファジークラスタリングとそのノイズに対するロバスト性,'' 信学論, J80-D-II, 1, pp. 190-197, 1997.
T. Taoda, K. Tsuda, and M. Minoh. Generating stereo images from a sequence of monocular images. In Proc. of Int. Conf. Virtual Systems and Multimedia, pages 368–373, 1996.
K. Tsuda and M. Minoh. Extracting straight lines by sequential fuzzy clustering. Pattern Recognition Letters, 17:643–649, 1996.
K. Tsuda, S. Senda, M. Minoh, and K. Ikeda. Clustering ocr-ed texts for browsing document image database. In Proc. 3rd Int. Conf. Document Analysis and Recognition, pages 171–174, 1995.