2020 Bioinformatics

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Academic unit or major
Graduate major in Artificial Intelligence
Instructor(s)
Akiyama Yutaka  Ishida Takashi 
Course component(s)
Lecture
Day/Period(Room No.)
Tue5-6(W833,G311)  Fri5-6(W833,G311)  
Group
-
Course number
ART.T543
Credits
2
Academic year
2020
Offered quarter
1Q
Syllabus updated
2020/3/24
Lecture notes updated
-
Language used
English
Access Index

Course description and aims

This course provides a comprehensive overview of bioinformatics where living matters are modeled and analyzed as information systems. The fundamental notions and methods in genome sequence analysis, protein structural bioinformatics, and cheminformatics are introduced with illustrative examples of recent research.
This course is aiming to show students live instances of computing technology application in our society, especially via the combination of various mathematical methods in order to extract meanings from vast and vague real-world data.

Student learning outcomes

By the successful completion of this course, students will be able to:
1) Explain fundamental knowledge on bioinformatics,
2) Explain novel mathematical methods to extract meanings from various data, and
3) Explain instances of computing technology application in society.

Keywords

Genome sequence analysis, protein structural bioinformatics, cheminformatics, computational biology

Competencies that will be developed

Intercultural skills Communication skills Specialist skills Critical thinking skills Practical and/or problem-solving skills

Class flow

Each class starts with the explanation of a new topic. In the class occasionally, students are given exercise problems to solve. Students are asked to submit final reports.

Course schedule/Required learning

  Course schedule Required learning
Class 1 Overview, Pairwise sequence alignment Genome sequence, Sequence analysis, Global/Local alignment
Class 2 Clustering, Phylogenetic tree Hierarchical clustering, Distance Matrix, Bootstrap
Class 3 Multiple sequence alignment, Sequence motifs Approximation methods for multiple alignments, Regular expression, Profile matrix, Hidden Markov model
Class 4 Sequence motifs (Cont'd), Coding region prediction Markov model, Hidden Markov model
Class 5 Homology search from databases E-value、P-value, FASTA、BLAST、PSI-BLAST
Class 6 Homology search from databases (Cont'd) , Sequence assembly BLAT, GHOST, Hamilton path, Eulerian path
Class 7 Protein structure comparison, structure classification Protein structure, structure-function relationship, structure comparison, structure classification
Class 8 Protein secondary structure prediction Protein structure prediction based on machine learning methods
Class 9 Protein tertiary structure prediction Comparative modeling, de novo prediction
Class 10 Protein docking simulation Protein-protein docking, protein-ligand docking, virtual screening
Class 11 Molecular simulation Molecular dynamics, quantum chemistry
Class 12 Comparison of chemical structure SMILES, SMART, molecular fingerprint, MCS
Class 13 Molecular activity prediction Neural fingerprint, graph convolution network
Class 14 Molecular design Generative model, VAE, GAN, reinforce learning

Textbook(s)

Original class slides are provided.

Reference books, course materials, etc.

Mount, David. Bioinformatics: Sequence and Genome Analysis (2nd edition). Cold Spring Harbor Laboratory Press; ISBN-13: 978-087969712-9

Assessment criteria and methods

Students' knowledge and their ability to apply them to solving problems will be assessed with final report.

Related courses

  • CSC.T242 : Probability Theory and Statistics
  • CSC.T353 : Biological Data Analysis
  • CSC.T272 : Artificial Intelligence
  • CSC.T352 : Pattern Recognition

Prerequisites (i.e., required knowledge, skills, courses, etc.)

None

Contact information (e-mail and phone)    Notice : Please replace from "[at]" to "@"(half-width character).

Yutaka Akiyama: akiyama[at]c.titech.ac.jp
Takashi Ishida: ishida[at]c.titech.ac.jp

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