Biomedical research, especially omics-related fields is complex due to the diversity in data, difficulty in interpreting these data and the availability of a large selection of tools that can perform identical operations with different performance levels. We study the important relevant features of biomedical analyses and propose an approach that models the domain using ontologies and that deals with the flexibility of tool selection using planning. We model an epigenomics task to find the correlation between mutation density with epigenetic marker density as a planning problem.
Gaya Nadarajan is a senior researcher at Seoul National University, South Korea with the Biomedical Knowledge Engineering (BiKE) Lab. Prior to SNU, she was a postdoctoral researcher at the University of Edinburgh, contributing on workflow management in the EU Fish4Knowledge project. She is currently interested in applying AI techniques to the biomedical domain, especially omics research.