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University of California San Diego
Genomic Data Science and Clustering (Bioinformatics V)
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  3. Health Informatics
University of California San Diego

Genomic Data Science and Clustering (Bioinformatics V)

This course is part of Bioinformatics Specialization

Pavel  Pevzner
Phillip Compeau

Instructors: Pavel Pevzner

Instructors

Instructor ratings

We asked all learners to give feedback on our instructors based on the quality of their teaching style.

4.0 (7 ratings)
Pavel  Pevzner
Pavel Pevzner
University of California San Diego
16 Courses•873,597 learners
Phillip Compeau
Phillip Compeau
University of California San Diego
8 Courses•308,419 learners

16,672 already enrolled

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3 modules
Gain insight into a topic and learn the fundamentals.
4.1

(93 reviews)

Intermediate level
Some related experience required
9 hours to complete
Flexible schedule
Learn at your own pace

3 modules
Gain insight into a topic and learn the fundamentals.
4.1

(93 reviews)

Intermediate level
Some related experience required
9 hours to complete
Flexible schedule
Learn at your own pace
  • About
  • Outcomes
  • Modules
  • Recommendations
  • Testimonials
  • Reviews

Skills you'll gain

  • Applied Machine Learning
  • Dimensionality Reduction
  • Machine Learning
  • Exploratory Data Analysis
  • Molecular Biology
  • Bioinformatics
  • Life Sciences
  • Data Analysis Software
  • Data Mining
  • Unsupervised Learning

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

3 assignments

Taught in English

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Build your subject-matter expertise

This course is part of the Bioinformatics Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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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&nbsp;<strong>clustering&nbsp;</strong>a set of data points&nbsp;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? &nbsp;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 videos•Total 32 minutes
  • (Check Out Our Wacky Course Intro Video!)•3 minutes•Preview module
  • Which Yeast Genes are Responsible for Wine Making? •6 minutes
  • Gene Expression Matrices •6 minutes
  • Clustering as an Optimization Problem •10 minutes
  • The Lloyd Algorithm for k-Means Clustering •5 minutes
2 readings•Total 10 minutes
  • Course Details•10 minutes
  • Week 1 FAQs (Optional)•0 minutes
1 assignment•Total 20 minutes
  • Week 1 Quiz•20 minutes
2 app items•Total 210 minutes
  • Open in order to Sync Your Progress: Interactive Text for Week 1•0 minutes
  • Interactive Text for Week 1•210 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 videos•Total 34 minutes
  • From Hard to Soft Clustering •11 minutes•Preview module
  • From Coin Flipping to k-Means Clustering •4 minutes
  • Expectation Maximization •7 minutes
  • Soft k-Means Clustering •3 minutes
  • Hierarchical Clustering •7 minutes
1 reading
  • Week 2 FAQs (Optional)•0 minutes
1 assignment•Total 15 minutes
  • Week 2 Quiz•15 minutes
2 app items•Total 210 minutes
  • Open in order to Sync Your Progress: Interactive Text for Week 2•0 minutes
  • Interactive Text for Week 2•210 minutes

What's included

2 readings1 assignment

2 readings•Total 20 minutes
  • Statement on This Week's Material•10 minutes
  • How Have Humans Populated the Earth?•10 minutes
1 assignment•Total 30 minutes
  • Week 3 Quiz•30 minutes

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Instructors

Instructor ratings

Instructor ratings

We asked all learners to give feedback on our instructors based on the quality of their teaching style.

4.0 (7 ratings)
Pavel  Pevzner
Pavel Pevzner
University of California San Diego
16 Courses•873,597 learners

Instructors

Instructor ratings

We asked all learners to give feedback on our instructors based on the quality of their teaching style.

4.0 (7 ratings)
Pavel  Pevzner
Pavel Pevzner
University of California San Diego
16 Courses•873,597 learners
Phillip Compeau
Phillip Compeau
University of California San Diego
8 Courses•308,419 learners

Offered by

University of California San Diego

Offered by

University of California San Diego

UC San Diego is an academic powerhouse and economic engine, recognized as one of the top 10 public universities by U.S. News and World Report. Innovation is central to who we are and what we do. Here, students learn that knowledge isn't just acquired in the classroom—life is their laboratory.

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Learner reviews

4.1

93 reviews

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H
HW
5

Reviewed on Jun 9, 2017

the part about EM is the best I know, and first time I understand the EM algorithm.

Z
ZX
5

Reviewed on Jul 20, 2019

In depth and comprehensive coverage of the topics in genetic data analysis.

J
JM
5

Reviewed on Mar 21, 2020

Really enjoyed the clustering chapters and the practical exercises with the yeast dataset

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