About 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.
Who is this class for: This course is primarily aimed at undergraduate-level learners in computer science, biology, or a related field who are interested in learning about how the intersection of these two disciplines represents an important frontier in modern science.
Taught by: Pavel Pevzner, Professor
Department of Computer Science and Engineering
Taught by: Phillip E. C. Compeau, Postdoctoral Researcher
Computer Science & Engineering, UC San Diego
| Basic Info |
Course 5 of 7 in the
Bioinformatics Specialization.
|
| Level | Beginner |
| Language | English |
| How To Pass | Pass all graded assignments to complete the course. |
| User Ratings |
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the part about EM is the best I know, and first time I understand the EM algorithm.
The first two week was very good, but for week 3 I don't see the content has any connection with Genomic Data Science.
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as all the other courses of the specialization, this course is addictive, intelligent, and you learn a lot