Systems genetic analyses make use of naturally occurring genetic polymorphisms to simultaneously map sources of genetic variation and identify relations among biological entities across biological scale (in this case miRNA, mRNA and behaviour).
Analysis included identifying themes among challenges and successes, identifying relations between statements related to any given theme, stating arguments and supporting them with evidence from the materials, making comparisons, and evaluating conclusions.
Such visualization facilitates identifying relations between features, relations between features and single/multiple phenotypes, and relations between phenotypes.
We divided identified relations into three groups: (i) incorrect relations, (ii) confirming previous observations and (iii) novel relations.
Although future studies are needed to ensure the effectiveness of this user-oriented approach, this study yielded promising results, demonstrating that user-supplied tags can be a helpful tool in selecting concepts to be included in a thesaurus and in identifying semantic relations among the selected concepts.
Additionally, these studies usually focused on one aspect of identifying the relations among drugs, pathways, and ADRs and did not consider the global map of ADRs and pathways.
This paper addresses an information-extraction problem that aims to identify semantic relations among medical concepts (problems, tests, and treatments) in clinical text.
First, our study design does not allow us to identify causal relations among the score differences.
Since naturally growing terminologies will contain synonymy, a certain approach to terminology work needs to be followed to identify synonymic relations among entries with identical terms.
Relation extraction supports the ability to identify specific relations among entities in the document (Task C: Annotation event detection).
