Extraction of coexpression relationship among genes from biomedical text using dynamic conditional random fields

Text mining tools and algorithms are being successfully used for information extraction especially on large corpus like biomedical publications. These tools not only aid in information extraction but also in forming new theories and relationships between various fields of biomedical research. Extraction of gene-gene or gene-disease relationship is one such application. In this paper, we introduce a method to detect coexpressed genes from text, using the grammatical dependencies among the words within sentences and Dynamic Conditional Random Fields (DCRFs). Determining the coexpression relationship between and among genes can help in identifying important concepts such as the functionality of gene(s) involved, their pathogenic mechanism, and in deciphering protein-protein interactions. This work attempts to extract relevant sentences by labeling the genes involved as well as the word representing the relationship, from full-length papers collected from PubMed. The results obtained were compared with that of Support Vector Machine (SVM) and Nearest Neighbor with generalization (NNge), and have been found to outperform both.