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Network Analysis in Systems Biology

Systems Biology and Biotechnology,

An introduction to data integration and statistical methods used in contemporary Systems Biology, Bioinformatics and Systems Pharmacology research. The course covers methods to process raw data from genome-wide mRNA expression studies (microarrays and RNA-seq) including data normalization, differential expression, clustering, enrichment analysis and network construction. The course contains practical tutorials for using tools and setting up pipelines, but it also covers the mathematics behind the methods applied within the tools. The course is mostly appropriate for beginning graduate students and advanced undergraduates majoring in fields such as biology, math, physics, chemistry, computer science, biomedical and electrical engineering. The course should be useful for researchers who encounter large datasets in their own research. The course presents software tools developed by the Ma’ayan Laboratory (http://labs.icahn.mssm.edu/maayanlab/) from the Icahn School of Medicine at Mount Sinai, but also other freely available data analysis and visualization tools. The ultimate aim of the course is to enable participants to utilize the methods presented in this course for analyzing their own data for their own projects. For those participants that do not work in the field, the course introduces the current research challenges faced in the field of computational systems biology.

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Rating 4.2 based on 21 ratings
Length 11 weeks
Effort 6-8 hours/week
Starts Jul 12 (15 weeks ago)
Cost $49
From Icahn School of Medicine at Mount Sinai via Coursera
Instructor Avi Ma’ayan, PhD
Download Videos On all desktop and mobile devices
Language English
Subjects Science Data Science
Tags Life Sciences Biology Data Science Data Analysis Bioinformatics Health Informatics Basic Science

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What people are saying

network analysis

also particularly appreciated there

I also particularly appreciated there being plenty of reference materials which are directly related to the content being provided at the end of every lecture.

systems biology specialization courses

From the Systems Biology specialization courses, this is the one from where I have learned the most, in some way the reason is because I didn't know most of Network Analysis, but now I feel familiarized with it.

really test your understanding

But I found many of the problems didn't really test your understanding of the material, just whether you remembered some fact from the lectures.

different bioinformatics or network-based

Also, have explained the working of different bioinformatics or network-based tools and software.

mayaan lab can

Perhaps a greater focus on offline techniques would be better if the Mayaan lab cannot invest the resources to maintain a bunch of servers.

explore particular areas

I am a biology graduate who is now doing a Masters in Bioinformatics and I found this course extremely helpful as it covers a wide scope of topics in a (relatively) short amount of time, providing necessary background and allowing students to go off and explore particular areas of interest.

needs few update

The course needs few update in terms of softwares/tools mentioned.

particularity disappointing by

Also, it's a shame that not all of the sites are completely functions - I was particularity disappointing by not being able to log on to the crowdsourcing site.

probably quite essentially

The topics were generally very well explained, but the "necessary but not required" prerequisites are probably quite essentially to getting grips with the course.

reference materials which

only downside

Only downside is that it oscillates between these explanations to very dry demonstrations of specific tools.

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Rating 4.2 based on 21 ratings
Length 11 weeks
Effort 6-8 hours/week
Starts Jul 12 (15 weeks ago)
Cost $49
From Icahn School of Medicine at Mount Sinai via Coursera
Instructor Avi Ma’ayan, PhD
Download Videos On all desktop and mobile devices
Language English
Subjects Science Data Science
Tags Life Sciences Biology Data Science Data Analysis Bioinformatics Health Informatics Basic Science

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