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Genomic Data Science and Clustering (Bioinformatics V)

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 t

4.1

14 reviews

Created by: Pavel Pevzner

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Overall Score : 82 / 100

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.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.

Time: 9h

Instructor Details

Pavel Pevzner

4.1 Rating
14 Reviews

Pavel Pevzner (https://cseweb.ucsd.edu/~ppevzner/) is Professor of Computer Science and Engineering at University of California San Diego (UCSD), where he holds the Ronald R. Taylor Chair and has taught a informatics Algorithms course for the last 12 years. In 2006, he was named a Howard Hughes Medical Institute Professor. In 2011, he founded the Algorithmic logy Laboratory in St. Petersburg, Russia, which develops online bioinformatics platform Rosalind (https://rosalind.info). His research concerns the creation of bioinformatics algorithms for analyzing genome rearrangements, DNA sequencing, and computational proteomics. He authored Computational Molecular logy (The MIT Press, 2000), co-authored (jointly with Neil Jones) An Introduction to informatics Algorithms (The MIT Press, 2004), and co-edited (with Ron Shamir) informatics for logists (Cambridge University Press, 2011). For his research, he has been named a Fellow of both the Association for Computing Machinery (ACM) and the International Society for Computational logy (ISCB).

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4.1

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By Daniel D on 24-Oct-19

Truly awesome. What I liked best was that this course didn't have a peer reviewed final challenge, so I didn't have to wait months until my work was graded :)

By Chan H Y on 27-Jul-19

This course is too Short and not up to date with deep learning

By Zack X on 21-Jul-19

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

By Milk on 4-Feb-19

A nice course to learn clustering algorithm in bioinformatics, it will be better if you take the previous class. Just jump into this class without some pre-knowledge will be quite difficult.

By Michael K on 11-Nov-18