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Medical Statistics and Bioinformatics Lab
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The lab uses statistics and bioinformatics to support the development of new treatments.
AboutAbout the lab
Supporting new treatments through statistics and bioinformatics
The Medical Statistics and Bioinformatics Lab combines medical statistics and bioinformatics to conduct research that contributes to developing effective new treatments, particularly personalized medicine.
The lab works to put multifaceted, complex information, such as biomarkers that predict treatment effects, to use in developing new treatments.
Motivated largely by solving problems that arise in collaborations with medical researchers, the lab trains people who can play an active role as members supporting clinical research.
Key terms
- Bayesian statistics
- An approach to statistics that combines prior knowledge with data and updates probabilities as it makes inferences.
- Biomarker
- A substance or piece of information in the body that offers clues about a disease state or how well a treatment is working.
- Patient-reported outcomes
- Assessments of health status, such as quality of life, reported by the patients themselves.
ResearchResearch themes
Using data science that combines medical statistics and bioinformatics, the lab conducts research that supports everything from early-stage clinical development to clinical research in an integrated way.
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MembersFaculty
- ProfessorSatoshi Morita
- Associate ProfessorHarue Tada
- LecturerYu Hidaka
- Assistant ProfessorKentaro Ueno
- Assistant ProfessorAki Kubota
- Program-Specific Assistant ProfessorTomohide Iwao
- Program-Specific Assistant ProfessorZixuan Yao
VoicesStudent voices
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Report an errorAbout the images
The top and research theme images are illustrative and were not taken at the lab. They are cropped from Wikimedia Commons images. Cropped CC BY-SA images are provided under the same CC BY-SA license as the original (version shown on each line).
- Top image: “Scholia - Clinical trials overview - 2021-12 - Phases” Wikimedia Community / CC BY-SA 4.0 (cropped)
- Statistics for New Clinical Trials and Treatment Effect Prediction: “P-value nomograph for Bayesian posterior estimation” Leonhard Held / CC BY 4.0 (cropped)
- Related lab (薬剤学): “Assistant Pharmacist” M Joko Apriyo Putro / CC BY-SA 4.0 (cropped)
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- Related lab (糖尿病・内分泌・栄養内科学): “Suckale08 fig2 islet structure” Jakob Suckale, Michele Solimena / CC BY 3.0 (cropped)
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