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RIKEN Center for Advanced Intelligence Project

Data-Driven Biomedical Science Team

Team Leader: Ichiro Takeuchi (D.Eng.)
Ichiro  Takeuchi(D.Eng.)

In the field of biomedical science, rapid advances in measurement technology allow us to collect a massive scientific dataset. An attempt aiming for a new scientific discovery based on such a massive scientific dataset is now realized as the fourth scientific paradigm followed by traditional three approaches based on theory, experiment, and simulation. Using artificial intelligence and machine learning techniques, we have a chance to find novel scientific hypotheses which are difficult to obtain only from knowledge and experiences of human experts. In our team, we study fundamental computational and mathematical techniques for data-driven scientific discovery, and demonstrate the effectiveness of these techniques in the field of biomedical science.

Main Research Field

Computer Science

Related Research Fields

Materials Sciences / Biology & Biochemistry / Molecular Biology & Genetics / Clinical Medicine / Mathematics

Research Subjects

  • Data Science

Selected Publications

Papers with an asterisk(*) are based on research conducted outside of RIKEN.
  1. *Toyoura K., Hirano D., Seko A., Shiga M., Kuwabara A., Karasuyama M., Shitara K., and Takeuchi I.:
    "Machine-learning-based selective sampling procedure for identifying the low-energy region in a potential energy surface: A case study on proton conduction in oxides"
    Physical Review B, 93 054112 (2016).
  2. *Nakagawa K., Suzumura S., Karasuyama M., Tsuda K., and Takeuchi I.:
    "Safe Pattern Pruning: An Efficient Approach for Predictive Pattern Mining"
    Proceedings of The 22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD2016) (2016).
  3. *Shibagaki A., Karasuyama M., Hatano K., and Takeuchi I.:
    "Simultaneous Safe Screening of Features and Samples in Doubly Sparse Modeling"
    Proceedings of The 33rd International Conference on Machine Learning (ICML2016) (2016).
  4. *Takeuchi I., Hongo T., Sugiyama M., and Nakajima S.:
    "Parametric Task Learning"
    Proceedings of The 27th Annual Conference on Neural Information Processing Systems (NIPS2013) (2013).
  5. *Karasuyama M., Harada N., Sugiyama M., and Takeuchi I.:
    "Multi-parametric solution-path algorithm for instance-weighted support vector machines"
    Machine Learning, 88, 297-330 (2012).
  6. *Takeuchi I., and Sugiyama M.:
    "Target neighbor consistent feature weighting for nearest neighbor classification"
    Proceedings of The 25th Annual Conference on Neural Information Processing Systems (NIPS2011) (2011).
  7. *Karasuyama M., and Takeuchi I.:
    "Multiple incremental decremental learning of support vector machines",
    IEEE Transactions on Neural Networks, 21, 1048-1059 (2010).
  8. *Takeuchi I., Tagawa H., Tsujikawa A., Nakagawa M., Katayama M., Guo Y., and Seto M:
    "The potential of copy number gains and losses, detected by array-based comparative genomic hybridization, for computational differential diagnosis of B-cell lymphomas and genetic regions involved in lymphomagenesis"
    Haematologica-The Hematology Journal, 94, 61-69 (2009).
  9. *Takeuchi I., Nomura K., and Kanamori T.:
    "Nonparametric conditional density estimation using piecewise-linear solution path of kernel quantile regression"
    Neural Computation, 21, 2, 533-559 (2009).
  10. *Takeuchi I., Le QV., Sears TD., and Smola AJ:
    "Nonparametric quantile estimation",
    Journal of Machine Learning Research, 7, 1231-1264 (2006).

Contact information

4F, Bldg.2, Nagoya Insitute of Technology, Gokiso-cho, Showa-ku,
Nagoya, Aichi,
466-8555, Japan

Email: takeuchi.ichiro [at] nitech.ac.jp

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