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.
Ce cours fait partie de la Spécialisation Bioinformatique
Genomic Data Science and Clustering (Bioinformatics V)
proposé par
Programme du cours : ce que vous apprendrez dans ce cours
Semaine
15 heures pour terminer
Week 1: Introduction to Clustering Algorithms
<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>
5 vidéos (Total 33 min), 2 lectures, 3 quiz
5 vidéos
Which Yeast Genes are Responsible for Wine Making? 6 min
Gene Expression Matrices 6 min
Clustering as an Optimization Problem 10 min
The Lloyd Algorithm for k-Means Clustering 5 min
2 lectures
Course Details10 min
Week 1 FAQs (Optional)
1 exercice pour s'entraîner
Week 1 Quiz20 min
Semaine
24 heures pour terminer
Week 2: Advanced Clustering Techniques
<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>
5 vidéos (Total 35 min), 1 lecture, 3 quiz
5 vidéos
From Hard to Soft Clustering 11 min
From Coin Flipping to k-Means Clustering 4 min
Expectation Maximization 7 min
Soft k-Means Clustering 3 min
Hierarchical Clustering 7 min
1 lecture
Week 2 FAQs (Optional)
1 exercice pour s'entraîner
Week 2 Quiz15 min
Semaine
31 heure pour terminer
Week 3: Introductory Algorithms in Population Genetics
2 lectures, 1 quiz
2 lectures
Statement on This Week's Material10 min
How Have Humans Populated the Earth?10 min
1 exercice pour s'entraîner
Week 3 Quiz10 min
4.2
11 avis33%
a bénéficié d'un avantage concret dans sa carrière grâce à ce cours
Meilleurs avis
the part about EM is the best I know, and first time I understand the EM algorithm.
Absolutely fantastic course. Kudos to the course creators.
Enseignants
Pavel Pevzner
ProfessorDepartment of Computer Science and Engineering
Phillip Compeau
Visiting ResearcherDepartment of Computer Science & Engineering
À propos de Université de Californie à 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.
À propos de la Spécialisation Bioinformatique
Join Us in a Top 50 MOOC of All Time!
How do we sequence and compare genomes? How do we identify the genetic basis for disease? How do we construct an evolutionary Tree of Life for all species on Earth?
When you complete this Specialization, you will learn how to answer many questions in modern biology that have become inseparable from the computational approaches used to solve them. You will also obtain a toolkit of existing software resources built on these computational approaches and that are used by thousands of biologists every day in one of the fastest growing fields in science.
Although this Specialization centers on computational topics, you do not need to know how to program in order to complete it. If you are interested in programming, we feature an "Honors Track" (called "hacker track" in previous runs of the course). The Honors Track allows you to implement the bioinformatics algorithms that you will encounter along the way in dozens of automatically graded coding challenges. By completing the Honors Track, you will be a bioinformatics software professional!
Learn more about the Bioinformatics Specialization (including why we are wearing these crazy outfits) by watching our introductory video.
You can purchase the Specialization's print companion, Bioinformatics Algorithms: An Active Learning Approach, from the textbook website.
Our first course, "Finding Hidden Messages in DNA", was named a top-50 MOOC of all time by Class Central!
Foire Aux Questions
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