| Academic Year |
2025Year |
School/Graduate School |
School of Informatics and Data Science |
| Lecture Code |
KA240701 |
Subject Classification |
Specialized Education |
| Subject Name |
バイオインフォマティクス |
Subject Name
（Katakana） |
バイオインフォマティクス |
Subject Name in
English |
Bioinformatics |
| Instructor |
EMURA TAKESHI |
Instructor
(Katakana) |
エムラ タケシ |
| Campus |
Higashi-Hiroshima |
Semester/Term |
3rd-Year, First Semester, Intensive |
| Days, Periods, and Classrooms |
(Int) Inte |
| Lesson Style |
Lecture |
Lesson Style
(More Details) |
Face-to-face |
| |
| Credits |
2.0 |
Class Hours/Week |
|
Language of Instruction |
B
:
Japanese／English |
| Course Level |
2
:
Undergraduate Low-Intermediate
|
| Course Area（Area） |
25
:
Science and Technology |
| Course Area（Discipline） |
02
:
Information Science |
| Eligible Students |
|
| Keywords |
Microarray, Classification, Statistical test |
| Special Subject for Teacher Education |
|
Special Subject |
|
Class Status
within Educational
Program
(Applicable only to targeted subjects for undergraduate students) | |
|---|
Criterion referenced
Evaluation
(Applicable only to targeted subjects for undergraduate students) | Computer Science Program
（Abilities and Skills）
・A. Information infrastructure development technology, information processing technology, technology that analyzes data and creates new added value.
Data Science Program
（Knowledge and Understanding）
・D1. Knowledge and ability to understand the theoretical framework of statistics and data analysis and to analyze qualitative/quantitative information of big data accurately and efficiently.
（Abilities and Skills）
・A. Information infrastructure development technology, information processing technology, technology that analyzes data and creates new added value.
・B. Ability to identify new problems independently and solve them through quantitative and logical thinking based on data, multifaceted perspectives, and advanced information processing and analysis.
Intelligence Science Program
（Abilities and Skills）
・A. Information infrastructure development technology, information processing technology, technology that analyzes data and creates new added value.
・B. Ability to identify new problems independently and solve them through quantitative and logical thinking based on data, multifaceted perspectives, and advanced information processing and analysis.
・D2. Information processing ability and data analysis ability to contribute to the application and development of artificial intelligence and IoT. |
|---|
Class Objectives
/Class Outline |
To understand DNA microarray analysis and relevant statistical tools.
To learn statistical methods to predict prognosis of patients based on high-dimensional gene expressions. |
| Class Schedule |
Lesson 1 DNA Microarray
Lesson 2 Microarray experiment
Lesson 3 Classification
Lesson 4 Detecting a difference based on the t-test
Lesson 5 Permutation test
Lesson 6 Controlling false positives
Lesson 7 Feature selection
Lesson 8 Discriminant analysis
Lesson 9 Compound covariate
Lesson 10 Decision tree
Lesson 11 Cross-validation
Lesson 12 Prognostic prediction
Lesson 13 Survival data
Lesson 14 Univeriate Cox regression
Lesson 15 Survival prediction |
Text/Reference
Books,etc. |
Simon, R. M. et al.(2003). Design and analysis of DNA microarray investigations. Springer Science & Business Media. |
PC or AV used in
Class,etc. |
|
| (More Details) |
|
| Learning techniques to be incorporated |
|
Suggestions on
Preparation and
Review |
Review statistics courses, including regression analysis and hypothesis test. Review the R software. |
| Requirements |
|
| Grading Method |
Sum of quizes and tests |
| Practical Experience |
|
| Summary of Practical Experience and Class Contents based on it |
|
| Message |
|
| Other |
|
Please fill in the class improvement questionnaire which is carried out on all classes.
Instructors will reflect on your feedback and utilize the information for improving their teaching. |