Using semantic workflows to disseminate best practices and accelerate discoveries in multi-omic data analysis

Yolanda Gil, Shannon McWeeney, Christopher E. Mason

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations


The goal of our work is to enable omics analysis to be easily contextualized and interpreted for development of clinical decision aids and integration with Electronic Health Records (EHRs). We are developing a framework where common omics analysis methods are easy to reuse, analytic results are reproducible, and validation is enforced by the system based on characteristics of the data at hand. Our approach uses semantic workflows to capture multi-step omic analysis methods and annotate them with constraints that express appropriate use for algorithms and types of data. This paper describes our initial work to use semantic workflows to disseminate best practices, ensure valid use of analytic methods, and enable reproducibility of omics analyses. Key elements of this framework are that it is knowledge-rich with regard to parameters and constraints that impact the analyses, proactive in the use of this knowledge to guide users to validate and correct their analyses and dynamic/adaptive as data sets evolve and change, all features that are critical for successful integration of omics analyses in a clinical setting.

Original languageEnglish (US)
Title of host publicationExpanding the Boundaries of Health Informatics Using Artificial Intelligence - Papers from the 2013 AAAI Workshop, Technical Report
PublisherAI Access Foundation
Number of pages6
ISBN (Print)9781577356202
StatePublished - Jan 1 2013
Event2013 AAAI Workshop - Bellevue, WA, United States
Duration: Jul 15 2013Jul 15 2013

Publication series

NameAAAI Workshop - Technical Report


Other2013 AAAI Workshop
Country/TerritoryUnited States
CityBellevue, WA

ASJC Scopus subject areas

  • Engineering(all)


Dive into the research topics of 'Using semantic workflows to disseminate best practices and accelerate discoveries in multi-omic data analysis'. Together they form a unique fingerprint.

Cite this