Explore the Library of Integrative Network-based Cellular Signatures (LINCS) and its application in perturbing human cells with diverse stimuli. This course, offered by Icahn School of Medicine at Mount Sinai, delves into Big Data Science with a focus on the BD2K-LINCS Data Coordination and Integration Center (DCIC).
Embark on a journey to comprehend the complexities of cellular responses to various perturbations and harness this knowledge for potential therapeutic predictions.
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This course provides a comprehensive overview of Big Data Science, covering topics such as LINCS program, metadata and ontologies, data serving with APIs, bioinformatics pipelines, data normalization, data clustering, enrichment analysis, machine learning, benchmarking, interactive data visualization, and crowdsourcing projects.
The Library of Integrated Network-based Cellular Signatures (LINCS) Program Overview provides insights into the layers of cellular regulation and omics technologies involved in LINCS. It also introduces the Connectivity Map and the BD2K-LINCS Data Coordination and Integration Center.
Metadata and Ontologies module introduces the role of metadata and ontologies in organizing and harmonizing LINCS data, emphasizing their importance in data integration and standardization.
Serving Data with APIs module delves into accessing and serving data through RESTful APIs, providing practical exercises to illustrate the application of these concepts.
Bioinformatics Pipelines module focuses on analyzing big data with computational pipelines, empowering learners with practical exercises to reinforce their understanding.
The Harmonizome module explores the concept of harmonizing datasets and provides in-depth coverage of data processing, complemented by practical exercises.
Data Normalization module equips learners with the knowledge and skills required for data normalization, complete with practical exercises for hands-on learning.
Data Clustering module introduces learners to the fundamentals of data clustering, covering distance functions, algorithms, and evaluation techniques, accompanied by practical exercises for application.
Enrichment Analysis module delves into the concept of enrichment analysis, providing insights into its application and showcasing the Enrichr tool through a demo.
Machine Learning module offers a comprehensive introduction to machine learning, empowering learners to apply machine learning techniques in the context of LINCS data, complemented by practical exercises.
Benchmarking module provides a detailed understanding of benchmarking techniques, preparing learners to assess the performance of computational methods and tools, supported by practical exercises.
Interactive Data Visualization module immerses learners in the world of interactive data visualization, offering practical exercises to hone their skills in visualizing complex datasets.
Crowdsourcing Projects module introduces learners to crowdsourcing and presents real-world applications through microtasks and demos, culminating in the BD2K-LINCS DCIC Crowdsourcing Portal.
Midterm Exam module offers learners the opportunity to assess their understanding of the course material through a comprehensive exam, providing a checkpoint for their progress.
Final Exam module evaluates learners' mastery of the course content, offering a comprehensive assessment to measure their knowledge and skills in Big Data Science with the BD2K-LINCS Data Coordination and Integration Center.
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