Subjects・Course Number
Laboratories
Language used （Japanese, English, Bilingual）
Availability for students from other lab.
Contact Information for Students from Other Lab.
Course Type
Year
Semester,Day and Period
Room Number
Credit
Course overview
(1) AI-based interpretation and prediction of genome functions
(2) Development of methods for analyzing single-cell and spatial omics data and their application to disease research
(3) Epigenome data analyses for regenerative medicine research
(4) Data science research on clinical information
Course Objectives (Learning Outcomes)
←SBO(Specific Behavior Objectives
1. Develop the ability to present the purpose, methods, results, interpretation of the results and future research plan of their bioinformatics and computational biology research.
2. Develop the ability to discuss the significance and originality of their experimental and/or statistical analyses in relation to the current understanding in the research field.
3. Develop the ability to understand questions and comments on their presentation, and use them for the improvement of their future research.
4. Develop the ability to understand the significance and limitations of other students’ presentations, and make useful suggestions for the improvement of their research.
5. Develop the ability to make research plan in compliance with the guidelines for the genomic and other data usage, under the supervision of the faculty member.
6. Develop the abilities to understand the methods and techniques in bioinformatics and computational biology, and to design, make proposals, and implement appropriate research projects in the relevant research area.
1. To be able to choose latest original articles to read by using major scientific magazines and on-line database.
2. To be able to understand the articles and explain the overview to other students in the fixed time.
3. To be able to understand other students’ explanation of the articles and discuss the significance and question of the research.
4. To be able to explain the historical significance and the position in the overall picture.
1. To be able to retrieve general data in the life science field from databases.
2. To be able to conduct general data preprocessing steps in the life science field.
3. To be able to conduct general data visualization methods in the life science field.
4. To be able to conduct general machine learning methods in the life science field.
5. To be able to conduct general statistical methods in the life science field.
Keyword
Course Schedule
第1回（月日、時限）担当教員名 講義内容など
Course prerequisite
Grading Criteria
Students are evaluated by their achievement of SBO.
Students who:
-acdieived SBO 1 and 2 are graded C or higher.
-achieved SBO 1and 2 and actively practicing 3 are graded B or higher.
-achieved 1, 2 and 3, and actively practicing 4- 6 are graded A or higher
- exhibited exceptional performance are graded A+ (top 10%).
Students are evaluated by their achievement of SBO
Students who:
-acdieived SBO 1 and 2 are graded C or higher.
-achieved SBO 1and 2 and actively practicing 3 are graded B or higher.
-achieved 1, 2 and 3, and actively practicing 4 are graded A or higher
- exhibited exceptional performance are graded A+ (top 10%).
Students are evaluated by their achievement of SBO
Students who:
-acdieived SBO 1 and 2 are graded C or higher.
-achieved SBO 1and 2 and actively practicing 3 are graded B or higher.
-achieved 1, 2 and 3, and actively practicing 4 and 5 are graded A or higher
- exhibited exceptional performance are graded A+ (top 10%).
Learning method
Out-of-class learning: Preparation for the presentation in the progress report.
Out-of-class learning: Preparation for the presentation in the journal club.
Out-of-class learning: Use the bioinformatic methods in your own study.
Textbook
Requirement to earn credit
Notes
Relation to Other Courses
Practice in Genome and Environmental Medicine I, II
Practice in Genome and Environmental Medicine I, II
Seminar in Genome and Environmental Medicine I, II