摘要：Preeclampsia is one of the most common causes of fetal and maternal morbidity and mortality in the world. We built a Database for Preeclampsia (dbPEC) consisting of the clinical features, concurrent conditions, published literature and genes associated with Preeclampsia. We included gene sets associated with severity, concurrent conditions, tissue sources and networks. The published scientific literature is the primary repository for all information documenting human disease. We used semantic data mining to retrieve and extract the articles pertaining to preeclampsia-associated genes and performed manual curation. We deposited the articles, genes, preeclampsia phenotypes and other supporting information into the dbPEC. It is publicly available and freely accessible. Previously, we developed a database for preterm birth (dbPTB) using a similar approach. Using the gene sets in dbPTB, we were able to successfully analyze a genome-wide study of preterm birth including 4000 women and children. We identified important genes and pathways associated with preterm birth that were not otherwise demonstrable using genome-wide approaches. dbPEC serves not only as a resources for genes and articles associated with preeclampsia, it is a robust source of gene sets to analyze a wide range of high-throughput data for gene set enrichment analysis.Database URL: http://ptbdb.cs.brown.edu/dbpec/
