Introduction to Bioinformatics for Drug Discovery
| Numbering Code | G-PHA02 51114 LJ86 | Year/Term | 2022 ・ Irregular, First semester | |
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| Number of Credits | 2 | Course Type | Lecture | |
| 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 general information science for students who studied life sciences such as pharmaceutical sciences as undergraduates and enrolled into the masters course of the department of bioinformatics and chemical genomics. More specifically, this course divides the entire information sciences into four parts: the foundations of information sciences, statistical sciences, algorithms, and knowledge sciences, and provides an overview of each part, making connections to medical and pharmaceutical sciences. Visiting lecturers will be invited to give presentations on their special research topics. Knowledge learnt through the lectures are practically confirmed by the exercises with computers. | |||
| Course Goals | To master a variety of information science technologies, covering from basics to applications necessary for bioinformatics and chemoinformatics. | |||
| Schedule and Contents |
Syllabus (1) Outline of the foundations of information sciences, particularly basic statistics. (2) Outline of the foundations of information sciences, particularly algorithms and data structure. (3) Outline of the foundations of information sciences, particularly programming languages. (4) Statistical sciences, particularly research outline of multivariate analysis and summary of research relevant to medical and pharmaceutical sciences. (5) Statistical science, particularly research outline of computational statistics and summary of research relevant to medical and pharmaceutical sciences. (6) Statistical science, particularly research outline of statistical models and time series analysis, and summary of research relevant to medical and pharmaceutical sciences. (7) Algorithms, particularly research outline of information theory-related technologies and summary of research relevant to medical and pharmaceutical sciences. (8) Algorithms, particularly research outline for strings and graphs and summary of research relevant to medicale and pharmaceutical sciences. (9) Algorithms, particularly research outline of numerical analysis and optimization techniques, and summary of research relevant to medical and pharmaceutical sciences. (10) Knowledge sciences, particularly research outline of knowledge engineering and inference technology, and summary of research relevant to medical and pharmaceutical sciences. (11) Knowledge sciences, particularly research outline of machine learning, and summary of research relevant to medical and pharmaceutical sciences. (12) Knowledge science, particularly research outline of databases, and summary of research relevant to medical and pharmaceutical sciences. (13) Exercise with computers regarding statistical sciences. (14) Exercise with computers regarding algorithms. (15) Exercise with computers regarding knowledge sciences. |
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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. |
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| References, etc. | Other textbooks may be specified due to the course content. | |||
