Introduction to Metadata and Ontologies

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.

CATEGORY: AI Awareness & Ontology

SUB-CATEGORY: Ontology

PROVIDER: Coursera


Ontology  |  Bio

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Course Format

Online

Accreditation Type

Certificate

Skill Level

Intermediate

Course Cost

This course is free

Introduction to Metadata and Ontologies

COURSE OVERVIEW

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.

2 hours to complete

The Library of Integrated Network-based Cellular Signatures (LINCS) Program Overview

24 minutes to complete

Bioinformatics Pipelines

1 hour to complete

Data Clustering

29 minutes to complete

Enrichment Analysis

26 minutes to complete

Benchmarking

19 minutes to complete

Crowdsourcing Projects

Final Exam

  • Basic understanding of ML
  • Understand Big Data concepts

Approx. 9 hours to complete


COURSE COMPLETION

A comprehensive understanding of computational methods including: data clustering, gene-set enrichment analysis, interactive data visualization, and supervised learning.

CREDIT BEARING

This course is NOT credit bearing

COURSE LICENCE

This course is available under Attribution-ShareAlike 2.0 South Africa