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课程简介
发表时间:2014-04-02 阅读次数:10813次

 

课程名称

课程内容

主讲教师

生物学基础及生物统计学基本方法与应用

Topic 1. 生物学基础知识 (3 学时) DNA, RNA and Proteins, Gene Regulation, Genetic Polymorphisms, Molecular Networks, Biological Databases.

Topic 2. Cellular Activity (3 学时) 钙信号,Single Channel Recording, 单神经元电生理记录,EEG,MRI,Brain Anatomy.

Topic 3. (4 学时) 观察性研究和试验性研究、不完全数据分析、属性数据分析.

Topic 4. (8 学时) 因果推断、因果网络.

Topic 5. (6 学时) 高维数据分析.

Topic 6. (8 学时) 时间序列数据分析——模型拟合、频谱分析、网络重构、预测和滤波、Data Assimilation. 

耿直、孙丰珠等

生物信息学概要及若干专题

Topic 1. The analysis of one molecular sequence (3 学时) 

Modeling DNA using Markov Chains, parameter estimation for Markov chains, substitution models, distribution of overlapping and non-overlapping sequence patterns, repeats in DNA sequences, r-scans, strong law of large numbers (SLLN) and central limit theorem for overlapping counts of patterns, renewal theory for non-overlapping patterns, motif discovery, expectation maximization algorithm.   

Topic 2. DNA sequencing and assembly (3 学时)  

Modeling project progress using Poisson processes, shotgun sequencing, single- and double-end clones, coverage, island- and gap-distributions, estimating sequence quality using Bayesian methods, applications to new generation sequencing, CHIP-Seq, RNA-Seq.   

Topic 3. Algorithms and statistics for sequence alignment (4 学时) 

Alignment of two sequences (scoring, global, local), Multiple alignment, Fast alignment to a database, RNA folding, extreme value distributions, Poisson approximation, the Chen-Stein method, BLAST statistics.    

Topic 4. Introduction to Hidden Markov Chains and their applications (4 学时) 

Markov models, Hidden Markov models (HMM), the forward and backward algorithms, the expectation and maximization (EM) algorithms, the Baum-Welch and Viterbi algorithms, the most probable path, posterior decoding, parameter estimation for HMM.  Profile HMM for sequence families, gene finding, semi-HMM with durations, promoters.   

Topic 5. Statistical genetics, genotype to phenotype mapping (4 学时) 

Genotypes, phenotypes, genome wide association studies (GWAS), rare variants, exome sequencing, regularization methods, data integration for genotype to phenotype mapping.   

Topic 6. Molecular networks, protein function prediction (4 学时) 

Experimental techniques for protein interaction detection, computational tools for predicting interactions, protein domain interactions, protein function, modules.   

Topic 7. Introduction to metagenomics (3 学时) 

The human microbiome project (HMP), environmental genomics, sequence read classification and binning, estimating genome abundance and gene families, linking to phenotypes.

Michael S. Waterman、孙丰珠等

系统生物学理论、方法及建模初步

Topic 1. 分子生物学 (9 学时) 酶、酶促反应动力学、代谢网络、信号传导、细胞活动.

Topic 2. 计算神经科学 (9 学时) 神经元及神经元网络模型、神经编码与解码.  

Topic 3. 种群遗传学 (9 学时) 群体平衡、自然选择、中性选择. 

冯建峰、丁明州、林伟等