Avi Ma’ayan, PhD, is the course director for two massive open online courses (MOOCs) on the Coursera platform. As of March 2016, more than 33,000 students registered for these two courses and they watched 195,000 video lectures.
Big Data Science with the BD2K-LINCS Data Coordination and Integration Center
In this course, students organize, analyze, visualize, and integrate LINCS data with other publicly available relevant resources. In this course, we discuss the various centers that collect data for LINCS, looking at the experimental data procedures and data types. We then cover the design and collection of metadata and how metadata is linked to ontologies, followed by basic data processing and data normalization methods to clean and harmonize LINCS data. We examine how the data is served as RESTful APIs and JSON, which involves exploring concepts from client-server computing. Most importantly, the course focuses on various bioinformatics methods of analysis including: unsupervised clustering, gene-set enrichment analyses, Bayesian integration, network visualization, and supervised machine learning applications to LINCS data and other relevant Big Data from molecular biomedicine.
Network Analysis in Systems Biology
This MOOC is an introduction to the 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-sequencing) including data normalization, differential expression, clustering, enrichment analysis, and network construction. We provide practical tutorials for using tools and setting up pipelines, and cover the mathematics behind the methods applied within the tools.
This course is mostly appropriate for beginning graduate students and advanced undergraduates majoring in fields such as biology, mathematics, physics, chemistry, computer science, and biomedical and electrical engineering. It would also be useful for researchers who encounter large datasets in their own research. The course presents software, applications, and tools developed by the Ma’ayan Laboratory as well as other freely available data analysis and visualization tools.
The aim of the course is to enable participants to use these methods for analyzing their own data for their own projects. For participants who do not work in the field, the course introduces the current research challenges in the field of computational systems biology.