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English
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Subs: English (.srt)
06:14:15
Genre: eLearning Video / Science, Bioinformatics

The Library of Integrative Network-based Cellular Signatures (LINCS) is an NIH Common Fund program. The idea is to perturb different types of human cells with many different types of perturbations such as: drugs and other small molecules; genetic manipulations such as knockdown or overexpression of single genes; manipulation of the extracellular microenvironment conditions, for example, growing cells on different surfaces, and more.

These perturbations are applied to various types of human cells including induced pluripotent stem cells from patients, differentiated into various lineages such as neurons or cardiomyocytes. Then, to better understand the molecular networks that are affected by these perturbations, changes in level of many different variables are measured including: mRNAs, proteins, and metabolites, as well as cellular phenotypic changes such as changes in cell morphology. The BD2K-LINCS Data Coordination and Integration Center (DCIC) is commissioned to organize, analyze, visualize and integrate this data with other publicly available relevant resources. In this course we briefly introduce the DCIC and the various Centers that collect data for LINCS. We then cover metadata and how metadata is linked to ontologies. We then present data processing and normalization methods to clean and harmonize LINCS data. This follow discussions about how data is served as RESTful APIs. Most importantly, the course covers computational methods including: data clustering, gene-set enrichment analysis, interactive data visualization, and supervised learning. Finally, we introduce crowdsourcing/citizen-science projects where students can work together in teams to extract expression signatures from public databases and then query such collections of signatures against LINCS data for predicting small molecules as potential therapeutics.

Content

Module 1 - The Library of Integrated Network-based Cellular Signatures LINCS Program Overview
Module 2 - Metadata and Ontologies
Module 3 - Serving Data with APIs
Module 4 - Bioinformatics Pipelines
Module 5 - The Harmonizome
Module 6 - Data Normalization
Module 7 - Data_Clustering
Module 8 - Enrichment Analysis
Module 9 - Machine Learning
Module 10 - Benchmarking
Module 11 - Interactive Data Visualization
Module 12 - Crowdsourcing Projects

Download File Size:523.24 MB

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