CSCI 3220 生物信息學中的算法 Algorithms for Bioinformatics

Course Code	CSCI 3220 CSCI3220			科目名稱		Algorithms for Bioinformatics 生物信息學中的算法
教員	Professor YIP Yuk Lap [官方介紹] [學術著作]			學　　分		3學分
課程性質				同科其他選擇
Workload	l   非PAPER類HOMEWORK l   MIDTERM l   FINAL EXAM								好重
									重
									平均	1
									輕
									極輕
評價教學內容	#1主要都係教同考algorithm, 唔識bio去讀都得, 雖然都會講吓bio 上嘅應用. Mid-term open notes, 但final close notes. 普遍exam題目都唔易, 需要好理解啲algorithm, 但相對佔分亦比assignment 少, 所以都合理 有部分topic 涉及statistics, 雖然佢都會重溫基本stat, 但建議之前都要少少stat知識 Assignment 有programming 嘅部份, 而且可以無限次submit 去system test
評價教員教學	#1講解詳細 (所以最後教唔哂...), 而且所有lecture 錄音會upload
CUSIS科目資料	Description： This course describes some algorithms commonly used in contemporary bioinformatics. After a brief introduction to basic molecular biology and genetics, four main topics will be covered with corresponding lists of algorithms: 1. Sequence alignment and assembly: dynamic programming for optimal sequence alignment, FASTA and BLAST for heuristic alignment of long sequences, tree-based methods for multiple sequence alignment, suffix-tree, suffix-array and Burrows-Wheeler Transform for short read alignment, and algorithms based on de Bruijn graphs for sequence assembly 2. Statistical modeling: forward, backward, Viterbi and Baum-Welch algorithms for hidden Markov models, Gibbs sampling for sequence motif discovery, and Bayesian classifiers, logistic regression and expectation-maximization for data classification and clustering 3. Phylogenetics: methods based on Jukes-Cantor and Kimura models for divergence time estimation, maximum parsimony, UPGMA, Neighbor-joining and maximum likelihood methods for phylogenetic tree reconstruction. 4. High-throughput data analysis: Hierarchical clustering and k-means for data clustering, and algorithms for selected problems such as signal peak calling, detection of gene fusion, haplotype phasing. Other topics such as RNA secondary structure prediction may also be covered depending on the available time. Learning Outcome： At the end of the course of studies, students will be able to 1. Explain the problems studied in both biological and computational terms. 2. Explain the details of the studied algorithms. 3. Apply the learned algorithms to solving both problems in bioinformatics and other domains. 4. Study new algorithms proposed for the same problems. 5. Understand contents of other classes in computational biology and bioinformatics.
其他資料	2017Sem1：學位 55|註冊 49|剩餘 6 2018Sem1：學位 60|註冊 54|剩餘 6
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