Most of the current biomedical knowledge from these large text collections is present in unstructured scientific text (journal publications, text fields in databases, e.g. more than 20 Mio. Documents in PubMed).
SCAIView provides users with full text and biomedical concept search capabilities, which are supported by large biomedical terminologies and ontologies, processed together with outstanding text mining technologies. Using machine learning and dictionary-based Named Entity Recognition (NER), SCAIView identifies information about genes, drugs, SNPs and other Life Science entities in MEDLINE abstracts and extracts this information. SCAIView uses a multi-threaded Lucene Index to allow semantic and ontological search on unstructured (text) data. Complex queries such as “what drugs are mentioned in the context of Alzheimers disease”? or “what genes are co-mentioned with Diabetes and are on the insulin signalling pathway”? can be asked in a user-friendly, intuitive way.
Please follow the following pages of the "First Steps" to get in touch with SCAIView!