Automatic Data Mining and Structuring for Research on Birth Defects

Biomedical knowledge usually reaches the end users with a considerable lag behind the newest published discoveries. Even if we manage to collect texts of new experimental studies or clinical experiments on any particular topic, the data quantity usually exceeds the human capacity to process the data in a reasonable time. The problem is more prominent in the case of clinical genetics - sometimes we need an information on a heart defect for one patient, an hour later about a kidney malformation for another patient etc., while preparation of targeted recherché with the help of the most modern bibliographic tools takes from days to weeks. Thus, patients do not get a diagnostic care on the highest achievable level even in the most developed countries in the world. The major prominent genetic databases are e.g.: OMIM (Online Mendelian Inheritance in Man), MGI, GenBank, Entrez Nucleotide, Entrez Genome, Gene Ontology, Sanger Center, EOL, EnsEMBL. None of the named databases supports synergistic collaboration with any other named database. This article describes a system that improves an information retrieval among data provided by biomedical database NCBI (National center for biotechnology information).