Lectures
Materials for lectures are available at SlideShare
<Bachelor course>
Exercise on Bioinformatics (JP, 1st semester spring term), for BC3 or BC4 (tentative plan: 1st and 2nd terms in June to August, 2023)
Keywords
scientific programming, probability theory, statistics, data science, bioinformatics, python, R
Purpose
1. Course Objectives
Life science, which aims to understand diverse life phenomena and apply unique macromolecular functions, isdrawing attention as an important field of natural sciences. The field of life sciences has become progressivelyinterdisciplinary, with the inclusion of biology, chemistry, physics and mathematics, leading to an understanding ofgreater depth than is possible through the study of individual basic sciences. A curriculum for developing skillsrequired to carry out research with a broad perspective in the field of life sciences is provided in this curriculum. Thecurriculum focuses on both basic and interdisciplinary sciences and learning will occur through a combination oflectures, exercises and experiments.
2. Course Goals
Informatics and statistical data analysis have become common due to recent rapid advance and popularization ofmeasurement and information technology. Applications of methods and tools for data analysis have widespread notonly in life-science research but also in various commercial activities such as genetic diagnosis. At the same time, avariety of opportunities and public resources for informatics and statistical analysis are now available online, helpingmotivated people to learn data analysis skills by themselves. Despite increasing the availability of such cloud-basedself-learning resources, it is still challenging to acquire data analysis skills without the guidance of skilled experts. Thisseries of lectures motivate participants to acquire basic skills of data analysis and related theories of statistics andprobability.
Goal
The series of lectures let participants acquire basic skills of data analysis and related theories of statistics andprobability.
- Lectures will help to understand elementary mathematics & information theory, and to acquire skills for appropriatechoice of data analysis methods.
- Hands-on training will help to acquire basic scientific programming skills.
Acquired skills will be useful to versatile applications for life-science research and advanced future services such asdisease diagnosis.
Course plan (access to the HU syllabus page for details)
1st Information processing and data structure
2nd Introduction to python and R
3rd Programming exercises with ptyhon and R (setup)
4th Programming exercises with ptyhon and R (basic operation)
5th Programming exercises with ptyhon and R (function)
6th Programming exercises with ptyhon and R (data handling)
7th Programming exercises with ptyhon and R (control flow)
8th Programming exercises with ptyhon and R (branching statements）
9th Programming exercises with ptyhon and R (control statements）
10th Descriptive statistics I (mean and variance etc)
11th Descriptive statistics I (histogram and visualization)
12th Probability theory basic I (discrete distribution)
13th Probability theory basic II (continuous distribution)
14th Inferential statistics I (sampling and estimation)
15th Inferential statistics II (hypothesis testing)
<Master course>
Informatics (elective subject, EN/JP hybrid) for faculty of Advanced Life Science, Department of Advanced Transdisciplinary Sciences, Tissue and Polymer Sciences (a series of eight lectures during three days on January 2024)
Keywords
Informatics, Computer programming, Probability theory and statistics, Machine learning, Bioinformatics, Genomic data
Purpose
Informatics and statistical analysis of omics data such as genomics and transcriptomics have become common due to recent rapid advance and popularization of next-generation sequencers. Applications of omics data analysis have widespread not only in life-science research but also in various commercial activities including genetic diagnosis. At the same time, a variety of opportunities and public resources for informatics and statistical analysis are now available online, helping motivated people to acquire data analysis skills. Despite increasing the availability of such cloud-based self-learning resources, it is still challenging to understand basic mathematics and informatics behind data analysis without the guidance of skilled experts. This series of lectures motivate participants not only to acquire data analysis skills of gene expression data but also to link practical methods for data analysis with underlying statistical and machine learning theories.
Goal
This series of lectures motivate participants not only to acquire data analysis skills of gene expression data but also to link practical methods for data analysis with underlying statistical and machine learning theories.
- Lectures will help (i) to understand elementary math & info methods, and (ii) to acquire skills for appropriate choice of analysis methods.
- Hands-on training will help to acquire basic scientific programming skill and to analyze gene expression data.
Acquired skills will be useful to versatile applications including omics-related research and advanced future services such as disease diagnosis.
Course plan (access to the HU syllabus page for details)
1st lecture: Introduction to omics data analysis
2nd lecture: Statistical analysis of count data
3rd lecture: Regression and statistical test
4th lecture: Hands-on programming (basic)
5th lecture: Hands-on programming (advanced)
6th lecture: Gene expression analysis (basic)
7th lecture: Gene expression analysis (advanced)
8th lecture: Special lecture