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Winter in Jokkmokk, northern Sweden

TEACHING

The greatest definition for concentration I ever hear is, "Wherever you are, be there!"  -- Jim Rohn


CISB366 Bioinformatics 

(Elective for CS major, 3 credit)
Bioinformatics is the study of biological information through computer modeling or analysis. Its goal is to reveal relationships between sequences, structures, and functions of molecules. In this course, we intend to give an broad introduction of the algorithmic techniques used in bioinformatics. Topics which will be covered include sequence similarity analysis, suffix tree, genome analysis, biological database search, phylogenetic analysis, protein structure manipulation and modeling.

This course is designed for undergraduate CS students and assumes no prior knowledge of molecular biology beyond the high school level. Basic concepts of molecular biology will be given in the first lecture, as well as in subsequent lectures before the computational problems are defined. The ultimate goal of this course is to prepare students with the knowledge and skills to conduct research in the area of bioinformatics.

CISB250 Human-Computer Interaction (HCI) 

(Elective for CS major, 3 credit) 
This course introduces fundamentals of interaction design based on established learning from human-computer interactions (HCI) in relation to contextual design of interactive systems. HCI is an important area of computing knowledge, and the construction of useful and usable interfaces that ease the man-machine interaction has become required skills for all computer science students. Coverage includes: problem formulation, user requirements study, usability analysis, prototyping, and evaluation. Pedagogy includes a mixture of dialogic teaching, classroom discussions of real cases, and group-based projects.

Computational Drug Discovery Techniques 計算藥物開發技術

For selected Macau high school students 

Computational drug discovery is an efficient and effective approach to drug discovery and development process. Due to the dramatic increase in the biological macromolecular structures and small molecule information, the applicability of computational techniques has been widely applied to every stage in the drug development workflow and basic science in drug research. In this course, we will briefly introduce the computational drug discovery process. Through lecturing, demonstration and case study, we will take a closer look at the sequence and molecular information, modeling algorithms, and state-of-the arts machine learning methods with applications on drug discovery. Topics include molecular three-dimensional structure visualization, protein-ligand docking and virtual screening for hit identification, molecular descriptors and classification model for antimicrobial peptides prediction.

計算藥物開發技術為加速及節省藥物開發過程中的有效策略。由於生物大分子和小分子資訊的可用性急劇增加,計算技術已被廣泛應用在藥物開發工作流程中的每一個階段以及在 藥物研究基礎科學上。在本課程中,我們將簡要地介紹計算藥物發現過程。透過教授,演示及案例分析我們將仔細討論序列和分子信息,建模算法,和機器學習方法及其在藥物開發中的應用。主題包括分子三維結構可視化,蛋白質配體對接和虛擬篩選的命中識別方法,以及利用分子描述值和分類模型來預測抗菌肽。