Darling (v2.0): Mining disease-related databases for the detection of biomedical entity associations - Fotis A. Baltoumas; Evangelos Karatzas; Nefeli K. Venetsianou; Eleni Aplakidou; Konstantinos Giatras; Maria N. Chasapi; Iro N. Chasapi; Ioannis Iliopoulos; Vassiliki A. Iconomidou; Ioannis P. Trougakos; Fotis Psomopoulos; Antonis Giannakakis; et al. (Computational and Structural Biotechnology Journal)
Darling is a web application that employs literature mining to detect disease-related biomedical entity associations. Darling can detect sentence-based cooccurrences of biomedical entities such as genes, proteins, chemicals, functions, tissues, diseases, environments, and phenotypes from biomedical literature found in six disease-centric databases. In this version, we deploy additional query channels focusing on COVID-19, GWAS studies, cardiovascular, neurodegenerative, and cancer diseases. Compared to its predecessor, users now have extended query options including searches with PubMed identifiers, disease records, entity names, titles, single nucleotide polymorphisms, or the Entrez syntax. Furthermore, after applying named entity recognition, one can retrieve and mine the relevant literature from recognized terms for a free input text. Term associations are captured in customizable networks which can be further filtered by either term or co-occurrence frequency and visualized in 2D as weighted graphs or in 3D as multi-layered networks. The fetched terms are organized in searchable tables and clustered annotated documents. The reported genes can be further analyzed for functional enrichment using external applications called from within Darling. The Darling databases, including terms and their associations, are updated annually. Darling is available at: https://www.darling-miner.org/.