In systems biology, inference of functional associations among genes is compelling because the construction of functional association networks facilitates biomarker discovery. Specifically, such gene associations in human can help identify
In systems biology, inference of functional associations among genes is compelling because the construction of functional association networks facilitates biomarker discovery. Specifically, such gene associations in human can help identify putative biomarkers that can be used as diagnostic tools in treating patients. Although biomedical literature is considered a valuable data source for this task, currently only a limited number of webservers are available for mining gene-gene associations from the vast amount of biomedical literature using text mining techniques. Moreover, these webservers often have limited coverage of biomedical literature and also lack efficient and user-friendly tools to interpret and visualize mined relationships among genes. To address these limitations, we developed GAIL (Gene-gene Association Inference based on biomedical Literature), an interactive webserver that infers human gene-gene associations from Gene Ontology (GO) guided biomedical literature mining. GAIL also provides dynamic visualization of the resulting association networks and various gene set enrichment analysis tools. This webinar will provide the overview of GAIL and illustrate its use for investigation and visualization of gene-gene networks using various examples including gene signatures associated with breast cancer.
The ITCR Program is a trans-NCI program supporting investigator-initiated, research-driven informatics technology development spanning all aspects of cancer research. The ITCR Program funds tools that support the analysis of -omics, imaging, and clinical data, as well as network biology and data standards. All of the tools are free for use by academic and non-profit researchers. Access to tools, code repositories, and introductory videos are available on the website.
(Tuesday) 11:00 am - 12:00 pm