An annotated corpus for extracting the phenotypic plasticity and the association of SNP-Phenotypes from the text

It has been known that SNPs are the most important types of genetic variations that can influence common diseases and phenotypes. Increasing number of SNP-phenotype related publications, demonstrate the need for an automatic extraction of this association from biomedical articles. Although few corpora have been developed for obtaining the mutation and disease from text, no corpus is available which has been annotated the level of confidence in associations which demonstrate the phenotypic plasticity. In this paper, different steps of producing SNPPhenA corpus were explained. They include gathering the abstracts, automatic and manual SNP and phenotype name tagging and annotating their associations. Additionally, the corpus includes negation scope and cues as well as neutral candidates that have important role in the relation extraction tasks. The inter-annotator agreement score for the confidence level which have been annotated by two annotators was between 0.70 and 0.85 that exhibit the reliability of the corpus. Additionally, an initial experiment was carried out on the corpus.