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Metabolism Study Reveals COVID Drug Targets

Learn pathway bioinformatics techniques to analyze metabolic networks and identify potential drug targets for SARS-CoV-2.

Learn pathway bioinformatics techniques to analyze metabolic networks and identify potential drug targets for SARS-CoV-2.

This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Applied Bioinformatics Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.

Instructors:

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Metabolism Study Reveals COVID Drug Targets

This course includes

8 Hours

Of Self-paced video lessons

Beginner Level

Completion Certificate

awarded on course completion

Free course

What you'll learn

  • Understand metabolic pathway analysis principles

  • Master pathway visualization techniques

  • Learn genome-based pathway inference methods

  • Analyze transcriptomics data

  • Predict essential genes using reachability analysis

  • Identify potential SARS-CoV-2 drug targets

Skills you'll gain

Pathway Bioinformatics
Metabolic Networks
Drug Target Analysis
SARS-CoV-2
Genome Analysis
Network Visualization
Essential Gene Prediction

This course includes:

7.8 Hours PreRecorded video

1 app item

Access on Mobile, Tablet, Desktop

FullTime access

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There is 1 module in this course

This specialized course explores pathway bioinformatics techniques for analyzing SARS-CoV-2 drug targets. Students learn about metabolic pathways, their computational representation, and visualization methods. The course covers genome-based pathway inference, transcriptomics data interpretation, and essential gene prediction through reachability analysis. Special focus is placed on analyzing human-SARS-CoV-2 metabolic interactions to identify potential drug targets.

Hacking COVID-19 — Course 4: Searching for Drug Targets

Module 1 · 8 Hours to complete

Fee Structure

Instructors

Niema Moshiri
Niema Moshiri

4.8 rating

26 Reviews

4,945 Students

5 Courses

Bioinformatics Researcher and Computer Science Educator

Niema Moshiri has transitioned from a Ph.D. student to an Associate Teaching Professor in the Department of Computer Science and Engineering at the University of California, San Diego. His academic journey began with a B.S. in Bioengineering: Bioinformatics, followed by a Ph.D. in Bioinformatics and Systems Biology from UCSD, where he was co-advised by Siavash Mirarab and Pavel Pevzner. His research focuses on computational biology, particularly in phylogenetics and phylogenomics, as well as computational viral epidemiology. As an educator, Moshiri has made significant contributions to computer science education, including co-authoring the interactive online textbook "Data Structures: An Active Learning Approach" and developing Massive Adaptive Interactive Texts (MAITs) for use in flipped classrooms and MOOCs. He is affiliated with the Department of Biomedical Informatics at the UC San Diego School of Medicine and the Halicioglu Data Science Institute. Moshiri's work extends to reviewing for numerous scientific journals and conferences in bioinformatics and computer science education.

Pavel Pevzner
Pavel Pevzner

8,33,777 Students

16 Courses

Pioneering Bioinformatics Scholar and Computational Biology Innovator

Pavel Arkadevich Pevzner serves as the Ronald R. Taylor Professor of Computer Science at the University of California, San Diego, and director of the NIH Center for Computational Mass Spectrometry, where he has revolutionized the field of computational biology since 2000. After receiving his Ph.D. in mathematics and physics from the Moscow Institute of Physics and Technology, he completed postdoctoral work with Michael Waterman at USC, before establishing himself through positions at Penn State and USC. His groundbreaking research spans bioinformatics algorithms, genome rearrangements, DNA sequencing, and computational proteomics, leading to significant advances in genome assembly and antibiotics discovery. His academic excellence has been recognized through numerous prestigious honors, including the Howard Hughes Medical Institute Professorship (2006), ACM Fellowship (2010), ISCB Fellowship (2012), and the ACM Paris Kanellakis Theory and Practice Award (2018). As an educator, he has transformed bioinformatics education through innovative approaches, including the development of massive open online courses that have reached over half a million students, and authored influential textbooks including "Computational Molecular Biology: An Algorithmic Approach" and "Bioinformatics Algorithms: An Active Learning Approach"

Metabolism Study Reveals COVID Drug Targets

This course includes

8 Hours

Of Self-paced video lessons

Beginner Level

Completion Certificate

awarded on course completion

Free course

Testimonials

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Frequently asked questions

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