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University of California, San Diego

Genomic Data Science and Clustering (Bioinformatics V)

University of California, San Diego via Coursera


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.

Taught by

Pavel Pevzner and Phillip Compeau


3.5 rating, based on 2 Class Central reviews

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  • Anonymous

    Anonymous completed this course.

    Highly recommend the course and the specializations to all learners who are serious about learning algorithms. This course goes deeply into developing hard and soft k mean clustering algorithms. Very tough course.
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    Alex Ivanov

    Alex Ivanov is taking this course right now.

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