Machine Learning and Machine Discovery: Methods and Practices

Our research interests primarily focus on two intertwined topics: (I) Machine learning with discrete structures, and (II) Machine discovery in data-centric natural sciences. Or we are exploring algorithms for "learning" and "discovery" in a principled way, using real-world natural sciences as a compelling testbed.

There is no science without data. Reproducibility and evidence are our basic premises. It is the nitty-gritty of every natural science what data are acquired (and how they are acquired) for hypothesis formation and verification. In recent years, we are being pushed to rethink and redefine how to make sense and make use of data as we witnessed the complication of research targets and/or societal requirements, large-scale experimentations and measurements, data/resource sharing via the Internet, and exponential growth in scientific publication with also often facing a reproducibility crisis.

(I) Machine learning with discrete structures. The methodological basis of current ML is grounded on data interpolation by functions on a vector space, but life science and materials science often come with non-numerical "discrete structure" not having direct vector representations. The examples include sets, lists, combinations, graphs, logical rules, languages, and programs. Such discrete structures are generated by composing finite objects. The real-world problems are filled with many problems involving discrete structures: the cases where the target itself has such a structure, for example, texts, genomic sequences, and chemical structures; the cases where the relationships between targets have such a structure, for example, intermolecular interactions, gene networks, and chemical reaction networks; the cases where the models have such a structure, for example, neural networks and decision trees/forests. Furthermore, we can consider wider scope as variations of discrete-structure problems, for example, explicit rules described by equations, mathematical symmetries such as spatial geometry and motion group, knowledge representations and planning, and symbol manipulation by languages. If we seriously think about it, all natural sciences we have now may not exist if we have no languages and symbols. Noam Chomsky raised a natural question known as "Plato's problem", i.e., the problem of explaining how we can know so much given our limited experience. Now it is also one of the central interests in artificial intelligence research to accord with this conflict between symbolic/discrete methods and statistical/continuous methods (or System 1 vs System 2 in words by Daniel Kahneman).

(II) Machine discovery in data-centric natural sciences. "Statistical learning from examples", the core technology for current "AI", predicts the future as an extension of the experienced past, and thus doesn't fit into the goal of natural sciences pursuing the understanding and discovery of something as-yet-unknown. Furthermore, the current ML is notoriously data-hungry, and requires an astronomical number of diverse training data that can cover all potential variations. This would be not only unrealistic in many labs, but also preposterous because we are not even clear about what potential variations exist and that's mainly why we try ML. In a sharp contrast, scientists have produced many understandings and discoveries from a very limited examples, which should be an ill-posed small-data problem from a statistical viewpoint. Of course, it would be supported by rich contextual information from all the past experiences, but also by the knowledge that is not directly experienced facts or rules (even not in an evolutionary sense; For instance, scientists read textbooks and papers, whereas current AI doesn't at least as they do). So we hypothesize that we need something non-empirical here, and empiricism or data-driven/inductive approach is only a part of the whole. We call it "discrete structures" that include language and logic. Now we also need new principled research on "machine discovery" about the mechanism of how we can reach discovery or understanding from learning. This clearly includes the topic (I) how to incorporate discrete structures in statistical ML and stochastic search, such as knowledge representations and planning by symbols and languages, deductive machineries such as logical inference, numerical simulation, combinatorial optimization, and mathematical symmetry such as spatial geometry on molecules and crystals, and thus (I) and (II) is tightly interlinked. Moreover, the methods developed in (I) can be verified by real-world problems of natural sciences via (II). The methodological research on the themes like (I) or (II) often results in self-contented abstract discussions, and therefore using natural science as a compelling testbed is quite desirable. On top of that, the developed methods in (I) can not only contribute to scientific discovery or understanding, but can also have a positive ripple effect on society and industry such as materials developments and drug discovery.

Research Areas

  • Machine learning
  • Machine discovery

Current Research Interests

  • Machine learning with discrete structures
    • Data representations: sets, lists, combinations, graphs, logical rules, languages, and programs.
    • Structural dependencies: interrelationship between variables/factors, structural constraints, network-shaped interactions.
    • Model structures: Decision trees/forests/diagrams, neural networks (computational graphs), hierachical probabilistic models, circuit design.
    • Gap between the continuum and the discrete, symbolic/logical/deductive machinery vs statistical/inductive machinery
  • Machine discovery in data-centric natural sciences
    • Problems on representing and intervening
    • Problems on correlation and causation
    • Problems on exploitation and exploration
    • Problems on creativity and randomness
    • Problems on hypothesis and methods (or, thought and language)
    • Fusing theory-driven and data-driven approaches
    • Bioinformatics, Chemoinformatics, Materials informatics

International Publications (→ Domestic publications → Talks)

[Book chapters] ↑Back

  • Machine learning predictions of adsorption energies of CH4-related species. [doi]
    Toyao T, Takigawa I, Shimizu K
    Direct Hydroxylation of Methane. 2020;135-149
  • SiBIC: a tool for generating a network of biclusters captured by maximal frequent itemset mining. [doi]
    Takahashi K, duVerle D, Yotsukura S, Takigawa I, Mamitsuka H
    Data Mining for Systems Biology: Methods and Protocols, Second Edition (Methods in Molecular Biology). 2018; 95-111
  • Machine learning predictions of factors affecting the activity of heterogeneous metal catalysts. [doi]
    Takigawa I, Shimizu K, Tsuda K, Takakusagi S
    Nanoinformatics. 2018;45-64
  • A bioinformatics approach for understanding genotype–phenotype correlation in Breast Cancer. [doi]
    Yotsukura S, Karasuyama M, Takigawa I, Mamitsuka H
    Big Data Analytics in Genomics. 2016;397-428
  • An in silico model for interpreting polypharmacology in drug–target networks. [doi]
    Takigawa I, Tsuda K, Mamitsuka H
    In Silico Models for Drug Discovery (Methods in Molecular Biology). 2013;993:67-80
  • Identifying pathways of coordinated gene expression. [doi]
    Hancock T, Takigawa I, Mamitsuka H
    Data Mining for Systems Biology (Methods in Molecular Biology). 2013;939:69-85

[Refereed journal papers] ↑Back

  • Graph network-based simulation of multicellular dynamics driven by concentrated polymer brush-modified cellulose nanofibers [doi]
    Yoshikawa C, Nguyen DA, Nakaji-Hirabayashi T, Takigawa I, Mamitsuka H
    ACS Biomaterials Science & Engineering, 2024; (Accepted)
  • Machine Learning Refinement of In Situ Images Acquired by Low Electron Dose LC-TEM [doi]
    Katsuno H, Kimura Y, Yamazaki T, Takigawa I
    Microscopy and Microanalysis, 2024;
  • Accelerated discovery of multi-elemental reverse water-gas shift catalysts using extrapolative machine learning approach [doi]
    Wang G, Mine S, Chen D, Jing Y, Ting KW, Yamaguchi T, Takao M, Maeno Z, Takigawa I*, Matsushita K, Shimizu K*, Toyao T*.
    Nature Communications, 2023; 14, 5861.
  • Gait video-based prediction of unified Parkinson’s disease rating scale score: a retrospective study [doi]
    Eguchi K, Takigawa I, Shirai S, Takahashi-Iwata I, Matsushima M, Kano T, Yaguchi H, Yabe I.
    BMC Neurology, 2023; 23, 358.
  • Machine Learning-Based Analysis of Molar and Enantiomeric Ratios and Reaction Yields Using Images of Solid Mixtures [doi]
    Ide Y*, Shirakura H, Sano T, Murugavel M, Inaba Y, Hu S, Takigawa I*, Inokuma Y*.
    Ind. Eng. Chem. Res., 2 2023; 62(35): 13790–1379.
  • Calcium sparks enhance the tissue fluidity within epithelial layers and promote apical extrusion of transformed cells [doi]
    Kuromiya K, Aoki K, Ishibashi K, Yotabun M, Sekai M, Tanimura N, Iijima S, Ishikawa S, Kamasaki T, Akieda Y, Ishitani T, Hayashi T, Toda S, Yokoyama K, Lee CG, Usami I, Inoue H, Takigawa I, Gauquelin E, Sugimura K, Hino N, Fujita Y.
    Cell Reports 2022; 40(2):111078.
  • Early detection of nucleation events from solution in LC-TEM by machine learning [doi]
    Katsuno H, Kimura Y, Yamazaki T, Takigawa I
    Frontiers in Chemistry 2022; 10:818230.
  • Machine learning analysis of literature data on the water gas shift reaction toward extrapolative prediction of novel catalysts [doi]
    Mine S, Jing Y, Mukaiyama T, Takao M, Maeno Z, Shimizu K, Takigawa I*, Toyao, T*
    Chemistry Letters 2022; 51(3), 269-273.
  • Fast improvement of TEM image with low-dose electrons by deep learning [doi]
    Katsuno H, Kimura Y, Yamazaki T, Takigawa I
    Microscopy and Microanalysis 2022; 28(1), 138-144.
  • A simplified methodology for the modeling of interfaces of elementary metals [doi]
    Hinuma Y, Takigawa I, Kohyama M, Tanaka S
    AIP Advances 2021; 11:115020.
  • Analysis of updated literature data up to 2019 on the oxidative coupling of Methane using an extrapolative machine-learning method to identify novel catalysts [doi]
    Mine S, Takao M, Yamaguchi T, Toyao T*, Maeno Z, Siddiki S M A H, Takakusagi S, Shimizu K*, Takigawa I*,
    ChemCatChem 2021; 13(16):3636-3655.
  • Minor-embedding heuristics for large-scale annealing processors with sparse hardware graphs of up to 102,400 nodes [doi]
    Sugie Y, Yoshida Y, Mertig N, Takemoto T, Teramoto H, Nakamura A, Takigawa I, Minato S, Yamaoka M, Komatsuzaki T.
    Soft Computing 2021; 25(3):1731-1749.
  • Frontier molecular orbital based analysis of solid-adsorbate interactions over group 13 metal oxide surfaces [doi]
    Liu C, Li Y, Takao M, Toyao T, Kamachi T, Hinuma Y, Takigawa I, Shimizu K.
    The Journal of Physical Chemistry C. 2020; 124(28): 15355–15365.
  • The role of Mediator and Little Elongation Complex in transcription termination. [doi]
    Takahashi H, Ranjan A, Chen S, Suzuki H, Shibata M, Hirose T, Hirose H, Sasaki K, Abe R, Chen K, He Y, Zhang Y, Takigawa I, Tsukiyama T, Watanabe M, Fujii S, Iida M, Yamamoto J, Yamaguchi Y, Suzuki Y, Matsumoto M, Nakayama KI, Washburn MP, Saraf A, Florens L, Sato S, Tomomori-Sato C, Conaway RC, Conaway JW, Hatakeyama S.
    Nature Communications. 2020; 11(1):1063.
  • Machine learning for catalysis informatics: Recent applications and prospects. [doi]
    Toyao T, Maeno Z, Takakusagi S, Kamachi T, Takigawa I*, Shimizu K*.
    ACS Catalysis. 2020; 10: 2260-2297. (Review Paper)
  • Dual graph convolutional neural network for predicting chemical networks. [doi]
    Harada S, Akita H, Tsubaki M, Baba Y, Takigawa I, Yamanishi Y, Kashima H
    BMC Bioinformatics. 2020; 21(Suppl 3):94. (From GIW2019)
  • Statistical analysis and discovery of heterogeneous catalysts based on machine learning from diverse published data. [doi] (Front Cover, Cover Profile)
    Suzuki K, Toyao T, Maeno Z, Takakusagi S, Shimizu K, Takigawa I.
    ChemCatChem. 2019; 11(18): 4537-4547.
  • Linear correlations between adsorption energies and HOMO levels for the adsorption of small molecules on TiO2 surfaces. [doi] (Cover)
    Kamachi T, Tatsumi T, Toyao T, Hinuma Y, Maeno Z, Takakusagi S, Furukawa, S, Takigawa I, Shimizu K.
    The Journal of Physical Chemistry C. 2019; 123(34): 20988-20997.
  • Density functional theory calculations of oxygen vacancy formation and subsequent molecular adsorption on oxide surfaces. [doi]
    Hinuma Y, Toyao T, Kamachi T, Maeno Z, Takakusagi S, Furukawa F, Takigawa I, Shimizu K.
    The Journal of Physical Chemistry C. 2018; 122(51): 29435–29444.
  • Toward effective utilization of methane: machine learning prediction of adsorption energies on metal alloys. [doi]
    Toyao T*, Suzuki K, Kikuchi S, Takakusagi S, Shimizu K, Takigawa I*.
    The Journal of Physical Chemistry C. 2018; 122(15): 8315-8326.
  • Obesity suppresses cell competition-mediated apical elimination of RasV12-transformed cells from epithelial tissues. [doi]
    Sasaki A, Nagatake T, Egami R, Gu G, Takigawa I, Ikeda W, Nakatani T, Kunisawa J and Fujita Y.
    Cell Reports. 2018; 23: 974-982.
    Press Release (Japanese)
  • Genomic copy number variation analysis in multiple system atrophy. [doi]
    Hama Y, Katsu M, Takigawa I, Yabe I, Matsushima M, Takahashi I, Katayama T, Utsumi J, Sasaki H.
    Molecular Brain. 2017; 10:54.
  • Machine learning reveals orbital interaction in materials. [doi]
    Pham T L, Kino H, Terakura K, Miyake T, Tsuda K, Takigawa I, Dam H C
    Science and Technology of Advanced Materials. 2017; 18(1): 756-765.
  • Generalized sparse learning of linear models over the complete subgraph feature set. [doi]
    Takigawa I, Mamitsuka H
    IEEE Transactions on Pattern Analysis and Machine Intelligence. 2017; 39(3): 617-624. (supplementary file)
  • An online self-constructive normalized Gaussian network with localized forgetting. [doi]
    Backhus J, Takigawa I, Imai H, Kudo M, Sugimoto M
    IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences. 2017; E100.A (3): 865-876.
  • The impact of income disparity on vulnerability and information collection: an analysis of the 2011 Thai flood. [doi]
    Henry M, Kawasaki A, Takigawa I, Meguro K
    Journal of Flood Risk Management. 2017; 10(3): 339-348.
  • Machine-learning prediction of d-band center for metals and bimetals. [doi]
    Takigawa I, Shimizu K, Tsuda K, Takakusagi S
    RSC Advances. 2016; 6: 52587-52595.
    highlighted in the article Machine-learning accelerates catalytic trend spotting (Chemistry World)
  • Exploring phenotype patterns of breast cancer within somatic mutations: a modicum in the intrinsic code. [doi]
    Yotsukura S, Karasuyama M, Takigawa I, Mamitsuka H
    Briefings in Bioinformatics. 2016. (Review Paper)
  • Dense core model for cohesive subgraph discovery. [doi]
    Kojaku S, Takigawa I, Kudo M, Imai H
    Social Networks. 2016; 44: 143–152.
  • Mining approximate patterns with frequent locally optimal occurrences. [doi]
    Nakamura A, Takigawa I, Tosaka H, Kudo M, Mamitsuka H
    Discrete Applied Mathematics. 2016; 200:123–152
  • Predictions of cleavability of calpain proteolysis by quantitative structure-activity relationship analysis using newly determined cleavage sites and catalytic efficiencies of an oligopeptide qrray [doi]
    Shinkai-Ouchi F, Koyama S, Ono Y, Hata S, Ojima K, Shindo M, duVerle D, Ueno M, Kitamura F, Doi N, Takigawa I, Mamitsuka H, Sorimachi H.
    Molecular & Cellular Proteomics. 2016; 15(4): 1262-80.
  • Ensemble and multiple kernel regressors: which is better? [doi]
    Tanaka A, Takebayashi H, Takigawa I, Imai H, Kudo M
    IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences. 2015; E98-A(11): 2315-2324.
  • The cell competition-based high-throughput screening identifies small compounds that promote the elimination of RasV12-transformed cells from epithelia. [doi]
    Yamaguchi H, Matsumaru T, Morita T, Ishikawa S, Maenaka K, Takigawa I, Semba K, Kon S, Fujita Y
    Scientific Report. 2015;15336.
  • MED26 regulates the transcription of snRNA genes through the recruitment of little elongation complex. [doi]
    Takahashi H, Takigawa I, Watanabe M, Anwar D, Shibata M, Tomomori-Sato C, Sato S, Ranjan A, Seidel C W, Tsukiyama T, Mizushima W, Hayashi M, Ohkawa Y, Conaway J W, Conaway R C, Hatakeyama S
    Nature Communications. 2015;6(5941).
    Press Release (Japanese)
  • Ribosomes in a stacked array: Elucidation of the step in translation elongation at which they are stalled during S-adenosyl-L-methionine-induced translation arrest of CGS1 mRNA. [doi]
    Yamashita Y, Kadokura Y, Sotta N, Fujiwara T, Takigawa I, Satake A, Onouchi H, Naito S
    Journal of Biological Chemistry. 2014;289(18):12693-704.
  • Similarity-based machine learning methods for predicting drug–target interactions: a brief review. [doi]
    H Ding, I Takigawa, H Mamitsuka, S Zhu
    Briefings in Bioinformatics. 2014;15(5):734-747 (Review Paper)
  • SiBIC: A web server for generating gene set networks based on biclusters obtained by maximal frequent itemset mining. [doi]
    Takahashi K, Takigawa I, Mamitsuka H
    PLoS One. 2013;8(12) e82890.
  • Fast algorithms for finding a minimum repetition representation of strings and trees. [doi]
    Nakamura A, Saito T, Takigawa I, Kudo M, Mamitsuka H
    Discrete Applied Mathematics. 2013;161(10-11):1556–1575
  • Graph mining: procedure, application to drug discovery and recent advances. [doi]
    Takigawa I, Mamitsuka H
    Drug Discovery Today. 2013;18(1-2):50-57 (Review Paper)
  • Identifying neighborhoods of coordinated gene expression and metabolite profiles. [doi]
    Hancock T, Wicker N, Takigawa I, Mamitsuka H
    PLoS One. 2012;7(2) e31345.
  • ROS-DET: robust detector of switching mechanisms in gene expression. [doi]
    Kayano M, Takigawa I, Shiga M, Tsuda K, Mamitsuka H
    Nucleic Acids Research. 2011;39(11): e74.
  • Mining significant substructure pairs for interpreting polypharmacology in drug-target network. [doi]
    Takigawa I, Tsuda K, Mamitsuka H
    PLoS One. 2011;6(2): e16999.
  • Efficiently mining delta-tolerance closed frequent subgraphs. [doi]
    Takigawa I, Mamitsuka H
    Machine Learning. 2011;82(2): 95-121.
  • A spectral approach to clustering numerical vectors as nodes in network. [doi]
    Shiga M, Takigawa I, Mamitsuka H
    Pattern Recognition. 2011;44(2): 236-251.
  • Mining metabolic pathways through gene expression. [doi]
    Hancock T, Takigawa I, Mamitsuka H
    Bioinformatics. 2010;26(17): 2128-2135.
  • On the performance of methods for finding a switching mechanism in gene expression. [doi]
    Kayano M, Takigawa I, Shiga M, Tsuda K, Mamitsuka H
    Genome Informatics. 2010;24: 69-83.
    (from the 10th Annual International Workshop on Bioinformatics and Systems Biology (IBSB2010), Kyoto, Japan, July 26-28, 2010)
  • Convex sets as prototypes for classifying patterns. [doi]
    Takigawa I, Kudo M, Nakamura A
    Engineering Applications of Artificial Intelligence. 2009;22(1): 101-108.
  • CaMPDB: a resource for calpain and modulatory proteolysis. [doi]
    duVerle D, Takigawa I, Ono Y, Sorimachi H, Mamitsuka H
    Genome Informatics. 2009;22: 202-214.
    (from the 9th Annual International Workshop on Bioinformatics and Systems Biology (IBSB2009), Boston, USA, July 27-29, 2009)
  • Efficiently finding genome-wide three-way gene interactions from transcript- and genotype-data. [doi]
    Kayano M, Takigawa I, Shiga M, Tsuda K, Mamitsuka H
    Bioinformatics. 2009;25(21): 2735-2743.
  • Field independent probabilistic model for clustering multi-field documents. [doi]
    Zhu S, Takigawa I, Zeng J, Mamitsuka H
    Information Processing & Management. 2009;45(5): 555-570.
  • Mining significant tree patterns in carbohydrate sugar chains. [doi]
    Hashimoto K*, Takigawa I*, Shiga M, Kanehisa M, Mamitsuka H (* equally contributed)
    Bioinformatics. 2008;24(16): i167-i173.
    (from ECCB'08 European Conference on Computational Biology, Cagliari, Italy, Sep 22-26, 2008)
  • Probabilistic path ranking based on adjacent pairwise coexpression for metabolic transcripts analysis. [doi]
    Takigawa I, Mamitsuka H
    Bioinformatics. 2008;24(2): 250-257.
  • Annotating gene function by combining expression data with a modular gene network. [doi]
    Shiga M, Takigawa I, Mamitsuka H
    Bioinformatics. 2007;23(13): i468-i478.
    (from the 15th Annual International Conference on Intelligent Systems for Molecular Biology (ISMB/ECCB 2007), Vienna, Austria, Jul 21-15, 2007)
  • Performance analysis of minimum L1-norm solutions for underdetermined source separation. [doi]
    Takigawa I, Kudo M, Toyama J
    IEEE Transactions on Signal Processing. 2004;52(3): 582-591.
  • The boosted/bagged subclass method.
    Takigawa I, Abe N, Shidara Y, Kudo M
    International Journal of Computing Anticipatory Systems. 2004;14: 311-320.
    (from the 6th International Conference on Computing Anticipatory Systems (CASYS'03), Liege, Belgium, Aug 11-16, 2003)

[Refereed conference papers] ↑Back

  • A ZDD-Based Method for Exactly Enumerating All Lower-Cost Solutions of Combinatorial Problems [link]
    Minato S, Banbara M, Horiyama T, Kawahara J, Takigawa I, Yamaguchi Y
    WEPA-2022 : Fifth Workshop on Enumeration Problems and Applications, 2-25 Nov 2022 Clermont-Ferrand, France, Nov 22-25, 2022.
  • Edit-aware generative molecular graph autocompletion for scaffold input [link]
    Hu S, Takigawa I, Xiao C
    The AAAI'22 Workshop on Deep Learning on Graphs: Methods and Applications (DLG-AAAI'22), Vancouver, BC, Canada, Feb 28, 2022.
  • Density functional theory calculations on surface oxygen vacancy formation in metal oxides. [link]
    Hinuma Y, Toyao T, Kamachi T, Maeno Z, Takakusagi, S, Furukawa S, Takigawa I, Shimizu K
    APS March Meeting 2020, Denver, Colorado, USA, March 6, 2020.
  • Efficiently enumerating substrings with statistically significant frequencies of locally optimal occurrences in gigantic string. [link]
    Nakamura A, Takigawa I, Mamitsuka H
    34th AAAI Conference on Artificial Intelligence (AAAI-20), New York, USA, February 7-12, 2020
  • Compiling higher order binary optimization problems into annealing processors. [link]
    Sugie Y, Mertig N, Iwata Y, Teramoto H, Nakamura A, Takigawa I, Minato S, Komatsuzaki T, Takemoto T
    25th International Symposium on Artificial Life and Robotics (AROB 25th 2020), Beppu, Japan, January 22-24, 2020
  • Learning relevant molecular representations via self-attentive graph neural networks. [link]
    Kikuchi S, Takigawa I, Oyama S, Kurihara M
    Workshop on Deep Graph Learning: Methodologies and Applications (DGLMA'19), IEEE BigData'19 Workshop, Los Angeles, USA, December 9, 2019
  • Dual graph convolutional neural network for predicting chemical networks. [link]
    Harada S, Akita H, Tsubaki M, Baba Y, Takigawa I, Yamanishi Y, Kashima H
    Joint 30th International Conference on Genome Informatics (GIW) and Australian Bioinformatics and Computational Biology Society (ABACBS) Annual Conference (GIW/ABACBS 2019), Sydney, Australia, December 9-11, 2019
  • Machine learning of the relationships between environment and climate variables and wildfire occurrences, and prediction of wildfires over Siberian region. [link]
    Yasunari TJ, Takigawa I, Kim KM, da Silva, AM
    The AGU 2018 Fall Meeting, Washington, D.C., USA, December 10-14, 2018
  • FPGA-based QBoost with large-scale annealing processor and accelerated hyperparameter search. [doi]
    Takemoto T, Mertig N, Hayashi M, Susa-Tanaka S, Teramoto H, Nakamura A, Takigawa I, Minato S, Komatsuzaki T, Yamaoka M.
    2018 International Conference on Reconfigurable Computing and FPGAs (ReConFig 2018), Cancun, Mexico, December 3–5, 2018
  • Graph minors from simulated annealing for annealing machines with sparse connectivity. [doi]
    Sugie Y, Yoshida Y, Mertig N, Takemoto T, Teramoto H, Nakamura A, Takigawa I, Minato S, Yamaoka M, Komatsuzaki T.
    The 7th International Conference on the Theory and Practice of Natural Computing (TPNC 2018), Dublin, Ireland December 12-14, 2018
  • Enumerating and indexing set partitions using sequence BDDs. [link]
    Takahashi S, Minato S, Takigawa I.
    2nd International Workshop on Enumeration Problems & Applications (WEPA 2018), Pisa, Italy, 5-8 November 2018
  • Jointly learning relevant subgraph patterns and nonlinear models of their indicators. [link]
    Shirakawa R, Yokoyama Y, Okazaki F, Takigawa I.
    The 14th International Conference on Mining and Learning with Graphs (MLG 2018) (KDD'18 Workshop), London, U.K., August 20, 2018
  • Online EM for the normalized Gaussian network with weight-time-dependent updates. [doi]
    Backhus J, Takigawa I, Imai H, Kudo M, Sugimoto M.
    The 23rd International Conference on Neural Information Processing (ICONIP 2016) Kyoto, Japan, October 16–21, 2016
  • Reducing redundancy with unit merging for self-constructive normalized Gaussian networks. [doi]
    Backhus J, Takigawa I, Imai H, Kudo M, Sugimoto M.
    The 25th International Conference on Artificial Neural Networks (ICANN 2016), Barcelona Spain, September 6-9, 2016.
  • Community change detection in dynamic networks in noisy environment. [WWW15]
    Koujaku S, Kudo M, Takigawa I, Imai H
    The 6th International Workshop on Modeling Social Media - Behavioral Analytics in Social Media, Big Data and the Web (MSM 2015), Florence, Italy, May 19, 2015
  • Theoretical analyses on ensemble and multiple kernel regressors. [JMLR proc]
    Tanaka A, Takigawa I, Imai H, Kudo M
    The 6th Asian Conference on Machine Learning (ACML2014), Nha Trang, Vietnam, November 26-28, 2014
  • Analyses on generalization error of ensemble kernel regressors. [doi]
    Tanaka A, Takigawa I, Imai H, Kudo M
    Proceedings of the Joint IAPR International Workshop on Statistical, Structural, and Syntactic Pattern Recognition (S+SSPR 2014), Joensuu, Finland, August 20-22, 2014.
    Lecture Notes in Computer Science, 2014;8621: 273-281.
  • Structual change point detection for evolutional networks. [link]
    Koujaku S, Kudo M, Takigawa I, Imai H
    Proceedings of the 2013 International Conference of Computational Statistics and Data Engineering, London, UK, July 3-5, 2013.
  • Extended analyses for an optimal kernel in a class of kernels with an invariant metric. [doi]
    Tanaka A, Takigawa I, Imai H, Kudo M
    Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition (SSPR&SPR 2012), Hiroshima, Japan, November 7-9, 2012.
    Lecture Notes in Computer Science, 2012;7627: 345-353.
  • Algorithms for finding a minimum repetition representation of a string. [doi]
    Nakamura A, Saito T, Takigawa I, Mamitsuka H, Kudo M
    Proceedings of the 17th symposium on String Processing and Information Retrieval (SPIRE2010), 185-190, Los Cabos, Mexico, Oct 11-13, 2010.
  • Classification by reflective convex hulls. [doi]
    Kudo M, Nakamura A, Takigawa I
    Proceedings of the 19th International conference on pattern recognition (ICPR2008), WeAT9.3, Tampa, Florida, USA, Dec 8-11, 2008.
  • A spectral clustering approach to optimally combining numerical vectors with a modular network. [doi]
    Shiga M, Takigawa I, Mamitsuka H
    Proceedings of the Thirteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2007), 647-656, San Jose, CA, USA, Aug 12-15, 2007.
  • A probabilistic model for clustering text documents with multiple fields. [doi]
    Zhu S, Takigawa I, Zhang S, Mamitsuka H
    the 29th European Conference on Information Retrieval (ECIR 2007), Roma, Italy, Apr 2-5, 2007.
    Lecture Notes in Computer Science, 2007;4425: 331-342.
  • Applying Gaussian distribution-dependent criteria to decision trees for high-dimensional microarray data. [doi]
    Wan R, Takigawa I, Mamitsuka H
    VLDB Workshop on Data Mining in Bioinformatics, Seoul, Korea, Sep 11, 2006.
    Lecture Notes in Computer Science, 2006;4316: 40-49.
  • The convex subclass method: combinatorial classifier based on a family of convex sets. [doi]
    Takigawa I, Kudo M, Nakamura A
    the IAPR International Conference on Machine Learning and Data Mining in Pattern Recognition (MLDM 2005), Leipzig, Germany, Jul 9-11, 2005.
    Lecture Notes in Computer Science, 2005;3587: 90-99.
  • Projection learning based kernel machine design using series of monotone increasing reproducing kernel hilbert spaces. [doi]
    Tanaka A, Takigawa I, Imai H, Kudo M, Miyakoshi M
    the 8th International Conference on Knowledge-Based Intelligent Information & Engineering Systems (KES2004), Wellington, New Zealand, Sep 20-24, 2004.
    Lecture Notes in Computer Science, 2004;3213: 1058-1064.
  • On the minimum L1-norm signal recovery in underdetermined source separation. [doi]
    Takigawa I, Kudo M, Nakamura A, Toyama J
    the 5th International Conference on Independent Component Analysis and Blind Signal Separation (ICA2004), Granada, Spain, Sep 22-24, 2004.
    Lecture Notes in Computer Science, 2004;3195: 193-200.
  • Error analysis of MAP solutions under Laplace prior in underdetermined blind source separation.
    Takigawa I, Kudo M, Toyama J, Shimbo M
    Proceedings of the Second International ICSC Symposium on Advances in Intelligent Data Analysis (AIDA'01), paper 1724-169, Bangor, U.K., June 19-22, 2001.
    (Proceedings CIMA'2001, ISBN 3-906454-26-6)
  • A modified LEGION using a spectrogram for speech segregation. [doi]
    Takigawa I, Kudo M, Toyama J, Shimbo M
    Proceedings of IEEE International Conference on Systems, Man, and Cybernetics (SMC'99), paper I 526-531, Tokyo, Japan, Oct 12-15, 1999.
    (ISBN 0-7803-5734-5, IEEE Catalog Number 99CH37028C)

[Other publications] ↑Back

  • Identifying neighborhoods of coordinated gene expression and metabolite profiles.
    Hancock T, Wicker N, Takigawa I, Mamitsuka H
    2012 International Workshop on Bioinformatics and Systems Biology (IBSB), Boston, July 22-26, 2012.
  • Mining significant substructure pairs in drug-target networks.
    Takigawa I
    2011 International Workshop on Bioinformatics and Systems Biology (IBSB), Berlin, Germany, July 17–20, 2011.
  • Parametric summarization of frequent subgraphs for characterizing structural features of bioactive compounds.
    Takigawa I, Mamitsuka H
    The 2010 Annual Conference of the Japanese Society for Bioinformatics (JSBi 2010), P109, Fukuoka, December 13-15, 2010.
  • Finding three-way gene interactions from transcript and genotype data.
    Kayano M, Takigawa I, Shiga M, Tsuda K, Mamitsuka H
    The 2010 Annual Conference of the Japanese Society for Bioinformatics (JSBi 2010), P69, Fukuoka, December 13-15, 2010.
  • Mining significant substructure-substructure pairs in structural associations.
    Takigawa I, Tsuda K, Mamitsuka H
    The 20th International Conference on Genome Informatics (GIW 2009), P107, Yokohama, December 14-16, 2009.
  • Genome-wide three-way gene interactions from transcript and genotype data.
    Kayano M, Takigawa I, Shiga M, Tsuda K, Mamitsuka H
    The 20th International Conference on Genome Informatics (GIW 2009), P045, Yokohama, December 14-16, 2009.
  • CaMPDB: a resource for calpain and modulatory proteolysis.
    du Verle D, Takigawa I, Ono Y, Sorimachi H, Mamitsuka H
    The 20th International Conference on Genome Informatics (GIW 2009), P114, Yokohama, December 14-16, 2009.
  • Efficiently finding significant substructural patterns conserved in glycans.
    Takigawa I, Hashimoto K, Shiga M, Kanehisa M, Mamitsuka H
    Proceedings of the 2008 annual conference of the Japaneses Society for Bioinformatics (JSBi2008), P066, Senri-Chuo, Osaka, December 15-16, 2008. (selected as oral talk T05)
  • Association of SNPs with multiple genes using a nonlinear regression model.
    Kayano M, Takigawa I, Shiga M, Tsuda K, Mamitsuka H
    Proceedings of the 2008 annual conference of the Japaneses Society for Bioinformatics (JSBi2008), P049, Senri-Chuo, Osaka, December 15-16, 2008.
  • Developing calpain substrate predictor with sequence information.
    Matsushima Y, Takigawa I, Ono Y, Sorimachi H, Mamitsuka H
    Proceedings of the 2008 annual conference of the Japaneses Society for Bioinformatics (JSBi2008), P071, Senri-Chuo, Osaka, December 15-16, 2008. (selected as oral talk T05)
  • A new method for clustering genes by optimally combining expression data with a modular gene network.
    Shiga M, Takigawa I, Mamitsuka H
    The 2007 Annual Conference of Japanese Society for Bioinformatics (JSBi2007), P037, Tokyo, December 17-19, 2007.
  • Probabilistic ranking for analyzing transcriptional response variations of metabolic gene paths.
    Takigawa I, Mamitsuka H
    The 17th International Conference on Genome Informatics (GIW2006), Yokohama, December 18-20, 2006.
  • Applying Gaussian distribution-dependent criteria to decision trees for high-dimensional microarray data.
    Wan R, Takigawa I, Mamitsuka H
    The 17th International Conference on Genome Informatics (GIW2006), Yokohama, December 18-20, 2006.
  • Extending multivariate Bernoulli and multinomial models for clustering MEDLINE records.
    Zhu S, Takigawa I, Zhang S, Mamitsuka H
    The 17th International Conference on Genome Informatics (GIW2006), Yokohama, December 18-20, 2006.
  • A gene clustering method using gene expression data and gene networks.
    Shiga M, Takigawa I, Mamitsuka H
    The 17th International Conference on Genome Informatics (GIW2006), Yokohama, December 18-20, 2006.
  • Combining vector-space and word-based aspect models for passage retrieval.
    Wan R, Ngoc Anh V, Takigawa I, Mamitsuka H
    Proceedings of 15th Text Retrieval Conference (TREC 2006), Gaithersburg, Maryland, Nov 14-17, 2006.
  • Ranking metabolic paths with expression similarities.
    Takigawa I, Mamitsuka H
    The 16th International Conference on Genome Informatics (GIW2005), Yokohama, December 19-21, 2005.
  • Subclass covering by balls for pattern classification.
    Takigawa I, Kudo M, Nakamura A
    Proceedings of The 2nd International Workshop on Ubiquitous Knowledge Network Environment, Sapporo, Japan, Mar 16-18, 2005.
  • The subclass method using adaptive sampling.
    Takigawa I, Abe N, Shidara M, Kudo M,
    Proceedings of The 1st International Workshop on Ubiquitous Knowledge Network Environment, Sapporo, Japan, Nov 25-27, 2003.