Pathway Studio Training
- Registration Closed
- Apr 11, 2018
- 09:30 AM to 02:30 PM
- NIH Library Training Room
Bioinformatics is an interdisciplinary field that develops methods and software tools for understanding biological data. As an interdisciplinary field of science, bioinformatics combines computer science, statistics, mathematics, and engineering to analyze and interpret biological data.
Do you need to do any of the following?
- Build molecular networks (of proteins, genes, small molecules, diseases, cells and cell processes, etc.) to identify signaling pathways or disease mechanisms?
- Analyze OMICS data (including gene expression, metabolomics, proteomics, genotypes) to find:
- cellular processes and ontologies
- major expression regulators
- interesting SNPs associated with specific phenotypes/diseases/cell processes/drugs
- Summarize a large body of literature on a specific subject such as a gene, cell process, disease, etc.?
- Generate publication quality images of biological pathways?
- Find what proteins are expressed in specific cell types or how a disease impacts a specific cell type?
Pathway Studio can help you do all this and more!
Join our Pathway Studio training session to learn how to use our online research solution that combines a vast knowledgebase of literature extracted molecular facts with powerful analytical and visualization tools to better understand the underlying biology from experimental, clinical and literature-based evidence.
The training session focus will be on de novo pathway building, high-throughput data analysis and variant analysis.
* Navigate Pathway Studio Web and utilize embedded tools for pathway analysis
* Create de novo pathways and biological association networks using the knowledgebase of facts extracted from scientific literature
* Import and analyze gene expression data and variant analysis
* Build new models or validate existing ones for diseases and cell processes
* Leverage the Pathway Studio knowledgebase to build research hypotheses and to verify findings
* Explore cause & effect interactions contained in your experimental data
* Fast report generation from expression data analysis.