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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NETWORK ANALYSIS IN SYSTEMS BIOLOGY의 최상위 리뷰
Various analytical approaches for network analysis are very well explained. Also, have explained the working of different bioinformatics or network-based tools and software.
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
It was a nice course with great information and resources for new people working or willing to work on bioinformatics
Excellent course to get deep into the data analysis of system biology experimentation.
Systems Biology and Biotechnology 특화 과정 정보
Design systems-level experiments using appropriate cutting edge techniques, collect big data, and analyze and interpret small and big data sets quantitatively.
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