Biomedical Statistics and Bioinformatics

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Satoshi Morita PhD, Professor

Current efforts of our department include taking advantage of huge and complex information of biomarkers such as genomic data. In response to this move, we are directing our full energy to carry out new researches that synthesize biomedical-statistics and bioinformatics. The primary goal of those research activities is supporting the development of new individualized therapies. In addition, we aim to provide medical researchers with a research environment for multi-purpose, multi-faceted information analysis using a comprehensive database consisting of data from basic research to early clinical trials, manufacturing, post marketing research, and epidemiological research.

Research and Education

Our research interests are on study designs and data analysis of early to late phase clinical trials. In biomedical statistics, we are mainly focusing on developing new Bayesian approaches. In bioinformatics, we aim to develop novel methods to analyze super high-dimensional data such as omics data and are performing collaborative research projects to find useful markers to predict the treatment effect. Furthermore, we are also interested in patient-reported outcome researches, e.g., quality of life assessment of cancer patients. Our department is supporting PhD and Master students who are willing to work on these research themes.

Recent Publications

  1. Yamamoto M, Hayashi K. Clustering of multivariate binary data with dimension reduction via L1-regularized likelihood maximization. Pattern Recognition. 48: 3959-3968, 2015.
  2. Morita S. Yamamoto H, Sugitani Y. Biomarker-based Bayesian randomized phase II clinical trial design to identify a sensitive patient subpopulation. Stat Med 33: 4008-4018, 2014.
  3. Morita S, Thall PF, and Mueller P. Prior effective sample size in conditionally independent hierarchical models. Bayesian Analysis. 7: 591-614, 2012.
  4. Morita S, Thall PF, Bekele BN, Mathew P. A Bayesian hierarchical mixture model for platelet derived growth factor receptor phosphorylation to improve estimation of progression-free survival in prostate cancer. Journal of the Royal Statistical Society, Series C (Applied Statistics). 59: 19-34, 2009.
  5. Morita S, Thall PF, Müller P. Determining the effective sample size of a parametric prior. Biometrics. 64: 595-602, 2008.

Biomedical Statistics and Bioinformatics

Professor Satoshi Morita:smorita(at)kuhp.kyoto-u.ac.jp
(Office: +81-75-751-4717, Fax: +81-75-751-4767)
Associate professor Harue Tada:haru.ta(at)kuhp.kyoto-u.ac.jp
Assistant professors
Michio Yamamoto:michyama(at)kuhp.kyoto-u.ac.jp
Kayoko Enomoto:kenomoto(at)kuhp.kyoto-u.ac.jp
Ryuji Uozumi:uozumi(at)kuhp.kyoto-u.ac.jp
Toshifumi Sugitani:sugitani(at)kuhp.kyoto-u.ac.jp
Naomi Akiyama:nakiyama(at)kuhp.kyoto-u.ac.jp
Secretary Phone:+81-75-751-3858, FAX:+81-75-751-4732
E-mail:secds01(at)kuhp.kyoto-u.ac.jp

Department of Data Science, Institute for Advancement of Clinical and Translational Science, Kyoto University Hospital
URL: -> Under construction