Genomic Data Science and Clustering (Bioinformatics V)

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Genomic Data Science and Clustering (Bioinformatics V)

Course Details

Cost

FREE

Upcoming Schedule

  • On demand

Course Provider

Coursera online courses
Coursera's online classes are designed to help students achieve mastery over course material. Some of the best professors in the world - like neurobiology professor and author Peggy Mason from the University of Chicago, and computer science professor and Folding@Home director Vijay Pande - will supplement your knowledge through video lectures. They will also provide challenging assessments, interactive exercises during each lesson, and the opportunity to use a mobile app to keep up with yo...
Coursera's online classes are designed to help students achieve mastery over course material. Some of the best professors in the world - like neurobiology professor and author Peggy Mason from the University of Chicago, and computer science professor and Folding@Home director Vijay Pande - will supplement your knowledge through video lectures. They will also provide challenging assessments, interactive exercises during each lesson, and the opportunity to use a mobile app to keep up with your coursework. Coursera also partners with the US State Department to create “learning hubs” around the world. Students can get internet access, take courses, and participate in weekly in-person study groups to make learning even more collaborative. Begin your journey into the mysteries of the human brain by taking courses in neuroscience. Learn how to navigate the data infrastructures that multinational corporations use when you discover the world of data analysis. Follow one of Coursera’s “Skill Tracks”. Or try any one of its more than 560 available courses to help you achieve your academic and professional goals.

Provider Subject Specialization
Humanities
Sciences & Technology
4719 reviews

Course 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.
Reviews 9/10 stars
1 Review for Genomic Data Science and Clustering (Bioinformatics V)

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Steven Frank profile image
Steven Frank profile image
10/10 starsCompleted
  • 59 reviews
  • 57 completed
3 years, 5 months ago
This fifth installment of the UCSD bioinformatics series is surprisingly short -- only two weeks of material, less than half that of most other installments. The biological topic is how we infer which genes orchestrate various processes in a cell. It continues concepts first introduced in part III -- gene duplications and genome rearrangements. Here the primary focus is on clustering techniques to identify genes likely to be expressed and regulated together and, therefore, collectively responsible for a particular biological phenomenon. These techniques, including k-means clustering, the E-M algorithm, and hierarchical and distance-based clustering, are interesting and lend themselves to straightforward coding. There's a fair bit of material covered in two weeks, but still, it's only two weeks, and in my session you had six weeks to complete the assignments. Depending on how the courses are scheduled, this one can readily be ta... This fifth installment of the UCSD bioinformatics series is surprisingly short -- only two weeks of material, less than half that of most other installments. The biological topic is how we infer which genes orchestrate various processes in a cell. It continues concepts first introduced in part III -- gene duplications and genome rearrangements. Here the primary focus is on clustering techniques to identify genes likely to be expressed and regulated together and, therefore, collectively responsible for a particular biological phenomenon. These techniques, including k-means clustering, the E-M algorithm, and hierarchical and distance-based clustering, are interesting and lend themselves to straightforward coding. There's a fair bit of material covered in two weeks, but still, it's only two weeks, and in my session you had six weeks to complete the assignments. Depending on how the courses are scheduled, this one can readily be taken in tandem with one of the others.
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