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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/genomic-data
课程评论：没有评论
课程名称：基因组数据科学与聚类（生物信息学V） 课程概述：在本课程中，我们将探讨如何推断哪些基因在细胞的各种过程中发挥作用，以及人类是如何从非洲迁出并传播到全球的。这两个看似不同的问题可以通过相似的算法和机器学习技术来解决，这些技术源于将数据点划分为不同聚类的通用问题。 课程的前半部分将介绍将一组对象基于相似性聚类成集群的算法，这是数据科学中的经典问题，并展示这些算法如何应用于基因表达数据。 课程的后半部分将介绍另一个经典的数据科学工具——主成分分析（PCA），该工具可以在聚类之前对多维数据进行预处理，以尽可能减少维度而不损失数据中的“大部分信号”。 最后，您将学习如何应用流行的生物信息学软件工具来解决实际的聚类问题。 课程大纲： - 第1周：聚类算法简介 描述：欢迎来到课程！本周，我们将看到聚类算法如何帮助我们确定酵母是如何成为优秀酿酒师的。 - 第2周：高级聚类技术 描述：在本周，我们将探讨如何从对点的“硬”分配转变为允许聚类边界融合的“软”分配，并学习Lloyd算法的适应性改进，以产生软聚类算法。 - 第3周：群体遗传学的入门算法 描述：本周将介绍群体遗传学中的一些基础算法。 通过此课程，您将掌握数据聚类的相关理论与实践，增强在生物信息学领域中的应用能力。
Name:Week 1: Introduction to Clustering Algorithms
Description:
Welcome to class!
At the beginning of the class, we will see how algorithms for clustering a set of data points will help us determine how yeast became such good wine-makers. At the bottom of this email is the Bioinformatics Cartoon for this chapter, courtesy of Randall Christopher and serving as a chapter header in the Specialization's bestselling print companion. How did the monkey lose a wine-drinking contest to a tiny mammal? Why have Pavel and Phillip become cavemen? And will flipping a coin help them escape their eternal boredom until they can return to the present? Start learning to find out!
Name:Week 2: Advanced Clustering Techniques
Description:
Welcome to week 2 of class!
This week, we will see how we can move from a "hard" assignment of points to clusters toward a "soft" assignment that allows the boundaries of the clusters to blend. We will also see how to adapt the Lloyd algorithm that we encountered in the first week in order to produce an algorithm for soft clustering. We will also see another clustering algorithm called "hierarchical clustering" that groups objects into larger and larger clusters.
Name:Week 3: Introductory Algorithms in Population Genetics
Description:
How do we infer which genes orchestrate various processes in the cell? How did humans migrate out of Africa and spread around the world? In this class, we will see that these two seemingly different questions can be addressed using similar algorithmic and machine learning techniques arising from the general problem of dividing data points into distinct clusters. In the first half of the course, we will introduce algorithms for clustering a group of objects into a collection of clusters based on their similarity, a classic problem in data science, and see how these algorithms can be applied to gene expression data. In the second half of the course, we will introduce another classic tool in data science called principal components analysis that can be used to preprocess multidimensional data before clustering in an effort to greatly reduce the number dimensions without losing much of the "signal" in the data. Finally, you will learn how to apply popular bioinformatics software tools to solve a real problem in clustering.
