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Avis et commentaires pour l'étudiant pour Analyse des réseaux dans la biologie systémique par Icahn School of Medicine at Mount Sinai

4.5
133 notes
17 avis

À propos du cours

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....

Meilleurs avis

FP

Jun 03, 2016

Excellent course to get deep into the data analysis of system biology experimentation.

CC

Apr 06, 2016

Its really a very interesting course ,and very informative

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1 - 17 sur 17 Examens pour Analyse des réseaux dans la biologie systémique

par Erick C O

May 11, 2016

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. I consider this is one of the most extended courses, and could be improved in the practice evaluation with more exercises.

par wiky

Jan 03, 2019

my favorite and the best course

par salvatore

Jan 30, 2016

Thanks - excellent!

par Woody S

Mar 28, 2016

demanding and interesting , like it ~

par Mac D G

Feb 05, 2018

helpful

par julio c r m

Nov 11, 2016

Excellent course .

par Felix E R P

Jun 03, 2016

Excellent course to get deep into the data analysis of system biology experimentation.

par Kirk G

Dec 02, 2016

Very good course

par C.RAMYA

Apr 06, 2016

Its really a very interesting course ,and very informative

par Alessandro F

Dec 17, 2018

Exciting course. I think some contents should be updated but in general an exhaustive overview.

par Páidí C

Mar 11, 2018

The course material was great, and covered many interesting topics. 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.

par Dmitry B

Apr 28, 2019

It was a good review of various tools, but maybe it was to many tools. I think it would be nice to show a smaller number of tools, but make more reproducible showcases

par Doreen B

Jun 05, 2019

Very good course. Subjects are explained very well. Only downside is that it oscillates between these explanations to very dry demonstrations of specific tools. That is not to say they are not demonstrated well and thoroughly, but I prefer the theoretical background. 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.

par Shubham G

Apr 30, 2016

It was interesting and informative course. However, I found difficulty in relating lessons and get true meaning of the course

par Mohamad K K

May 08, 2016

Lacks the hands on learning experience i was looking for but a great overview.

par Ahuno S T

Jan 05, 2016

very insightful

par Alok S

Oct 19, 2019

The content of the class was interesting. However many of the resources described do not work properly. Perhaps a greater focus on offline techniques would be better if the Mayaan lab cannot invest the resources to maintain a bunch of servers.