Genomic Data Science and Clustering (Bioinformatics V)
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Genomic Data Science and Clustering (Bioinformatics V)
This course is part of Bioinformatics Specialization
Instructors: Pavel Pevzner
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There are 3 modules in this course
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.
<p>Welcome to class!</p><p>At the beginning of the class, we will see how algorithms for <strong>clustering </strong>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 <a href="http://bearandfox.com" target="_blank" title="Link: http://bearandfox.com">Randall Christopher</a> and serving as a chapter header in the Specialization's bestselling <a href="http://bioinformaticsalgorithms.com" target="_blank">print companion</a>. 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!</p><p><img width="550" alt="" src="http://bioinformaticsalgorithms.com/images/cover/clustering_cropped.jpg" title="Image: http://bioinformaticsalgorithms.com/images/cover/clustering_cropped.jpg"></p>
What's included
5 videos2 readings1 assignment2 app items
5 videosβ’Total 33 minutes
- (Check Out Our Wacky Course Intro Video!)β’3 minutes
- Which Yeast Genes are Responsible for Wine Making? β’6 minutes
- Gene Expression Matrices β’7 minutes
- Clustering as an Optimization Problem β’11 minutes
- The Lloyd Algorithm for k-Means Clustering β’5 minutes
2 readingsβ’Total 10 minutes
- Course Detailsβ’10 minutes
- Week 1 FAQs (Optional)β’0 minutes
1 assignmentβ’Total 20 minutes
- Week 1 Quizβ’20 minutes
2 app itemsβ’Total 210 minutes
- Interactive Text for Week 1β’210 minutes
- Open in order to Sync Your Progress: Interactive Text for Week 1β’0 minutes
<p>Welcome to week 2 of class!</p> <p>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.</p>
What's included
5 videos1 reading1 assignment2 app items
5 videosβ’Total 35 minutes
- From Hard to Soft Clustering β’12 minutes
- From Coin Flipping to k-Means Clustering β’5 minutes
- Expectation Maximization β’8 minutes
- Soft k-Means Clustering β’3 minutes
- Hierarchical Clustering β’8 minutes
1 reading
- Week 2 FAQs (Optional)β’0 minutes
1 assignmentβ’Total 15 minutes
- Week 2 Quizβ’15 minutes
2 app itemsβ’Total 210 minutes
- Interactive Text for Week 2β’210 minutes
- Open in order to Sync Your Progress: Interactive Text for Week 2β’0 minutes
What's included
2 readings1 assignment
2 readingsβ’Total 20 minutes
- Statement on This Week's Materialβ’10 minutes
- How Have Humans Populated the Earth?β’10 minutes
1 assignmentβ’Total 30 minutes
- Week 3 Quizβ’30 minutes
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Reviewed on Jun 9, 2017
the part about EM is the best I know, and first time I understand the EM algorithm.
Reviewed on Jul 20, 2019
In depth and comprehensive coverage of the topics in genetic data analysis.
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Absolutely fantastic course. Kudos to the course creators.
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