Skill Development for Chemoinformatics
| Numbering Code | G-PHA02 75002 PJ86 | Year/Term | 2022 ・ Irregular, year-round | |
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| Number of Credits | 4 | Course Type | Practical training | |
| Target Year | 1st and 2nd year master's students | Target Student | ||
| Language | Japanese | Day/Period | ||
| Instructor name |
MAMITSUKA HIROSHI (Institute for Chemical Research Professor) Canh Hao Nguyen (Institute for Chemical Research Senior Lecturer) |
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| Outline and Purpose of the Course | This course offers lectures covering the information processing technologies essential for drug discovery informatics for students who enrolled in the masters course of the department of bioinformatics and chemical genomics. The objective is to master the basic knowledge and background technologies behind various technologies in bioinformatics and chemoinformatics through practical exercises. | |||
| Course Goals | To master practical skills on basic knowledge and background technologies in bioinformatics and chemoinformatics. | |||
| Schedule and Contents |
Syllabus 1-15: following items will be provided through practical exercises: ・Current status of bioinformatics research. ・Current status of chemoinformatics research. ・Literature summary of sequence alignment tools. ・Exercises in sequence alignment tools. ・Literature summary of sequence matching technologies. ・Exercises in sequence matching tools. ・Literature summary of 3D structure analysis technologies. ・Detailed understanding on 3D structure analysis technologies. ・Exercises in 3D structure analysis technologies. ・Literature summary of machine learning and knowledge discovery technologies. ・Detailed understanding of research on machine learning and knowledge discovery technologies. ・Exercises in machine learning and knowledge discovery technologies. ・Literature summary and exercises in randomized algorithms and other statistical and probabilistic technologies. ・Detailed understanding of research on randomized algorithms and other statistical and probabilistic technologies. ・Exercises in randomized algorithms and other statistical and probabilistic technologies. |
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| Evaluation Methods and Policy | Attendance record and reports on the topics of the course content. | |||
| Course Requirements | None | |||
| Study outside of Class (preparation and review) | Self study on the course content. | |||
| Textbooks | Textbooks/References | Lecture materials might be handed out. | ||
| References, etc. | Other textbooks may be specified due to the course content. | |||
