Text phrase-mining in identifying and classifying maternal proteins and genes across preeclampsia and similar pathologies.

ABSTRACT: This study aims to demonstrate that text phrase-mining and natural language processing (NLP) can annotate huge quantities of obstetrics textual data for the discovery and evaluation of maternal protein/gene (MPG)-disease interactions involved in the preeclampsia pathway. We employ a phrase-mining/NLP pipeline to evaluate unique MPGs involved in six cardiovascular derangements with overlapping presentations during pregnancy. The diseases were matched with Medical Subject Headings. A textual corpus was developed from abstracts matched to these terms through PubMed. Fourty-four MPGs were identified with respect to the diseases. Processing was performed, with unique scores for each MPG-disease pair. Components of the score were calculated and weighted for distinctness, integrity, and popularit

SUBMITTER: Urdang JG 

PROVIDER: S-EPMC11919630 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature