This course addresses essential data mining techniques such as clustering,
classification, association, and network inference, and explains their
applications to various bioinformatics problems including gene expression
analysis, disease diagnosis, and biosystem reverse engineering.
Formal representation and analysis of bio-processes including metabolic
pathways, signal transduction pathways, and regulation networks are examined.
After broadening the understanding of formal representation tools such as
graphs, Boolean networks, and Bayesian networks, individual research projects
for bio-network modeling are carried out.
This course focuses on understanding computational algorithms and techniques
for fundamental bioinformatics problems including sequence alignment, motif
analysis, gene prediction, and database search. The lectures will provide
computational principles of those bioinformatics techniques, and the students
are required to implement the algorithms by themselves.