Structured Data Resources for Autism Spectrum Disorder
The output of published research is growing at an exponential rate, making it difficult for researchers to keep up to date with even a fraction of new findings. Automated curation of biomedical literature has become necessary to deal with this influx of new information and allows researchers to leverage this information for further innovation. Multiple studies show that automated curation achieves best results when developed with respect to some specific domain. This project proposes an approach for the annotation of literature focusing on Autism Spectrum Disorders. Autism Spectrum Disorders have a complex genetic basis, wide variability in symptomology and are largely heterogeneous with a number of co morbid disorders, making exact characterization a difficult task. The approach proposed here follows a pipeline of biomedical entitiy recognition, identifying autism phenotypes and extraction of relationships between entities, using Natural Language Processing with various open source tools and resources. The system is evaluated against manually annotated data and shows performance scores comparable to previous systems. Finally, the system is deployed on a corpus of ASD relevant articles, allowing us to examine the prevalence of ASDrisk genes and phenotypes, identify relationships between them, and observe patterns among the annotated papers.