Extracting Alternative Splicing Information from Captions and Abstracts Using Natural Language Processing

Alternative splicing mechanisms provide protein diversity for cellular growth and development. In this research, we generate a database to descript alternative splicing in different circumstance from captions and abstracts in open access journals by using natural language processing techniques. We use medical subject headings(MeSH) to tag words, and extract the AS mechanism information by UMLS semantic network. In this database, AS information in genes on tissue-specificity, disease-related, developmental stage, functional implications, splicing type and species are contained.