生物信息计算IIBioinformatics Computing II |
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课程网址: | https://ocw.vu.edu.pk/CourseDetails.aspx?cat=Bioinformatics&cours... |
主讲教师: | Dr. Muhammad Haroon Khan |
开课单位: | 巴基斯坦虚拟大学 |
开课时间: | 信息不详。欢迎您在右侧留言补充。 |
课程语种: | 英语 |
中文简介: | 自然计算的动机,哲学,分支介绍。计算机中自然的模拟和仿真,自然材料的计算,自然计算方法,自然现象,模型和隐喻I&II,自然现象,从自然到计算再返回,个体,实体和代理,并行性和分布性,交互性,连通性,Stigmergy,采用,进化I&II反馈,正反馈,负反馈,自组织及其表征和替代方案,复杂性,出现,还原论,自下而上与自上而下,确定性,混沌,分形,生理系统建模入门,仿真和控制I,生理系统建模,模拟和控制的综合定义II,系统的输入和输出,建模,计算机功能的演变以及生理系统建模的进展,项目建设的现象。生理组I,II及其数据库,从分子到人类构建大模型的策略I,构建大模型II的策略,包括实例的虚拟人类生理学(VPH)计划。心脏工作量,建模级别,类型和分类,确定性模型,随机模型,参数模型,非参数模型,分区模型,详细分区模型以及类型和示例。生理控制系统的线性建模,包括实例;生理控制系统的非线性建模;生理系统建模,仿真和控制的未来;专业社团和组织;生物启发性计算;生命的信息组织并孕育了它的出现,解释和信息;生命的逻辑机制,自然界中的信息和信息过程的本质,形式化知识:揭示自然,自我组织和新兴复杂行为的设计原理,混沌边缘的生活,复杂的自我组织,进化计算,进化生物学,进化论,达尔文思想,遗传学基本原理,遗传学原理,作为变异和选择的结果进化,进化的经典例子,进化论的吸引力,进化论的支柱,基因型,细胞复制:有丝分裂,细胞复制:减数分裂,遗传突变,进化计算,Ar原始进化I&II,标准进化算法,遗传编码,二值实值和树表示,可演化性,适应度函数,人口,选择算子选择压力,遗传漂移,按比例选择,轮盘赌轮等级和截断等级基于选择,锦标赛选择Elitism ,遗传算子,交叉,变异,幸存者选择,初始化和终止,进化测度,进化和遗传算法(GAs)传统搜索和优化方法的鲁棒性,优化目标,遗传算法和传统搜索方法,遗传算法的要素,遗传算法,一个简单的GA I&II正在运行:手工I,GA正在进行工作:II模拟,GA的应用,遗传编程(GP),遗传编程的挑战,基于树的GP中遗传编程表示的进展,初始化种群,选择,重组和突变,遗传编程步骤,并附有示例。 p> 进化规划,进化规划算子,进化策略I&II,群智能,蚁群优化I&II(带示例),粒子群优化I&II,Bees算法,细菌觅食优化算法,神经网络,生物神经网络和人工神经网络,感知器I&II,反向传播I&II,Hopfield网络I&II,学习矢量量化,自组织图I&II,人工神经网络的优缺点及其在生物信息学中的应用,人工生命概论,人工生命领域的最新发展,ALife的历史和理论根源,软件人造生命,硬件人造生命,湿软件人造生命,人工细胞,自主代理,数字进化,Stiquito:六足昆虫机器人I,Salamandra Robotica I&II,人工免疫系统,生物免疫系统,免疫网络理论,否定选择机制,克隆选择原则iple,入侵检测系统,初始化/编码,相似或相似性度量,阴性,克隆或邻域选择,体细胞超突变,人工免疫系统与遗传算法和神经网络的比较,危险理论,人工免疫系统中的危险理论,一些有希望的领域用于AIS的应用程序,DNA计算,DNA计算和DNA计算机的概念,为什么进行DNA计算,DNA的基础,DNA计算的唯一性,DNA计算的动机,DNA计算的一般工作方面,信息存储和处理能力,效率,成功计算的原理,第一部分:生成所有可能的路线,第二部分:选择以正确的城市开始和结束的路线,第三部分:选择包含正确城市数的路线,第四部分:选择包含正确城市数的路线完整的城市,读出答案,注意事项,DNA计算的应用,DNA与常规电子计算机的比较,DNA的优势通勤,DNA计算的缺点,字符串匹配,近似字符串匹配,动态编程(DP),序列比对,逐对序列比对,全局与局部比对,全局比对基础,初始化和矩阵填充,跟踪,实例示例,局部比对,初始化和矩阵填充,回溯,实践示例,成本函数的重要性,多序列比对(MSA),生物动机,评分多序列比对,MSA动态规划算法,渐进式比对方法,星形比对,复杂度分析,锻炼, ClustalW,T Coffee,决策树,节点和分支,分类树,示例,在计算生物学中的应用 p> |
课程简介: | Introduction to Natural Computing its Motivation,philosophy,branches. The simulation and emulation of nature in computers,Computing with natural materials,Natural computing approaches, Natural Phenomena, Models and metaphores-I&II, Natural Phenomena, From nature to computing and back again, Individuals, entities and agents, Parallelism and distributivity, Interactivity, Connectivity, Stigmergy, Adoptation, Evolution-I&II Feedback, Positive feedback, Negative feedback, Self-organization and its Characterization and Alternatives, Complexity, Emergence, Reductionism, Bottom-up vs top-down, Determinsim, Chaos, Fractals, Introduction to physiological systems modeling, Simulation, and control-I ,Comprehensive definition of physiological systems modeling,simulation, and control-II,Input and the output of a system, Modeling, Evolution of Computer Power and Advancements in Physiological Systems Modeling ,Construction of projects phenomenon. Physiome-I,II and its databases, From molecules to humankind Strategies Toward Constructing Large Models-I,Strategies Toward Constructing Large Models-II,The Virtual Physiological Human (VPH) initiative with examples. The Cardiome Effort,Levels of modeling, types and classifications, Deterministic models,Stochastic models,Parametric models,Non-parametric models, Compartmental Modeling, Detailed Compartmental Models and types and examples. Linear modeling of physiological control system with examples, Nonlinear modeling of physiological control systems, The future of physiological systems modeling, simulation, and control,Professional societies and organizations,Bio-inspired computation,Life its Information Organizes and Breeds its Emergence, Explanation and Information,The Logical Mechanisms of Life,The Nature of Information and Information Processes in Nature,Formalizing Knowledge: Uncovering the Design Principles of Nature,Self-Organization and Emergent Complex Behavior,Life on the Edge of Chaos?,Complex Self-organization,Evolutionary Computing,Evolutionary Biology,On the theory of Evolution,The Darwins Idea,Basic Principles of Genetics,Principles of Genetics in detail,Evolution as an outcome of Variation and Selection,A classic example of evolution,The appeal of evolution Pillars of Evolutionary theory,The genotype ,Cell Replication: Mitosis,Cell Replication: Meoisis,Genetic mutations ,Evolutionary computation ,Artificial Evolution-I&II, Standard Evolutionary Algorithms,Genetic encoding ,Binary Real-Valued and Tree based representation, Evolvability, Fitness Functions,Population,Selection Operators Selection Pressure, Genetic drift,Proportionate selection,Roulette wheel Rank based and Truncated rank based selection ,Tournament selection Elitism ,Genetic operators,Crossover ,Mutation ,Survivor selection, Initialization and termination, Evolutionary measures , Evolutionary and Genetic Algorithms (GAs) Robustness of traditional search and optimization methods, The goals of optimization, GA and traditional search methods ,Elements of GAs , GA operators, A simple GA -I&II at work: A simulation by hand-I, GA at work: A simulation by hand-II, Applications of GAs ,Genetic Programming (GP), Genetic Programming Challenges, Progress in Genetic Programming Representation in Tree-based GP, Initialising the Population, Selection, Recombination and Mutation, Genetic Programming steps with examples. Evolutionary Programming, Evolutionary Programming operators, Evolution Strategies-I&II, Swarm Intellegence, Ant colony optimization-I&II with Example, Particle Swarm Optimization-I&II, Bees Algorithm,Bacterial Foraging Optimization Algorithm, Introduction to Neural Networks, Biological Neural Network and Artificial Neural Networks ,Perceptron-I&II,Back-propagation-I&II, Hopfield Network-I&II,Learning Vector Quantization,Self-Organizing Map-I&II, Advantages and disadvantages of artificial neural networks and applications in Bioinformatics, An introduction to Artificial Life, Recent developments in the field of Artificial Life,Historical & theoretical roots of ALife,Software artificial life,Hardware artificial life, Wetware artificial life,Artificial cells,Autonomous agents, Digital evolution, Stiquito: A Hexapod Insectoid Robot-I,Salamandra Robotica-I&II,Artificial Immune Systems, Biological immune system,Immune Network Theory,Negative Selection mechanism,Clonal Selection Principle,Intrusion Detection Systems,Initialization / Encoding,Similarity or Affinity Measure,Negative, Clonal or Neighbourhood Selection,Somatic Hypermutation,Comparison of artificial immune systems to genetic algorithms and neural networks,Danger Theory,Danger Theory in Artificial Immune Systems,Some promising areas for application for AIS,DNA Computing,Concepts of DNA computing and DNA computer,Why DNA Computing,Basics of DNA,Uniqueness of DNA computing,Motivation for DNA computing,General working aspects of DNA computing,Information storage and processing capabilities,Efficiency,Success of DNA computing,How it works,Part I: Generate all possible routes,Part II: Select itineraries that start and end with the correct cities,Part III: Select itineraries that contain the correct number of cities,Part IV: Select itineraries that have a complete set of cities,Reading out the answer,Caveats,Applications of DNA computing,Comparison of DNA and conventional electronic computers,Advantages of DNA comuting,Disadvantages of DNA computing,String Matching,Apprximate string matching,Dynamic Programming (DP),Sequence alignment,Pairwise sequence alignment,Global vs Local Alignment,Global Alignment Fundamentals,Initializtion and Matrix filling,Trace Back,Practicle Example,Local Alignment,Initializtion and Matrix filling,Trace Back,Practicle Example,Importance of Cost Functions,Multiple Sequence Alignment (MSA),Biological Motivation,Scoring a multiple sequence alignment,Dynamic Programming Algorithm for MSA,Progressive alignment approaches,Star Alignment,Complexity Analysis,Exercise,ClustalW,T-Coffee,Decision Trees,Nodes and Branches,Classification Trees,An Example,Applications to computational biology |
关 键 词: | 自然计算; 序列比对; 计算生物学 |
课程来源: | 巴基斯坦虚拟大学公开课 |
数据采集: | 2021-04-20:nkq |
最后编审: | 2021-04-20:nkq |
阅读次数: | 81 |