@article{3219178,
    title = "Darling: A Web Application for Detecting Disease-Related Biomedical Entity Associations with Literature Mining",
    author = "Karatzas, E. and Baltoumas, F.A. and Kasionis, I. and Sanoudou, D. and Eliopoulos, A.G. and Theodosiou, T. and Iliopoulos, I. and Pavlopoulos, G.A.",
    journal = "Ancient Biomolecules",
    year = "2022",
    volume = "12",
    number = "4",
    publisher = "MDPI",
    issn = "1358-6122",
    doi = "10.3390/biom12040520",
    keywords = "growth differentiation factor 15;  protein, Article;  bibliographic database;  bioinformatics;  book;  cardiovascular disease;  cytokine production;  darling database;  data integration;  data mining;  disease association;  disease ontology;  genetic risk;  growth rate;  intellectual impairment;  medical terminology;  Medline;  obesity;  online system;  phenotype;  Roberts syndrome;  scientific literature;  software, Data Mining;  Phenotype;  Proteins;  PubMed;  Software",
    abstract = "Finding, exploring and filtering frequent sentence-based associations between a disease and a biomedical entity, co-mentioned in disease-related PubMed literature, is a challenge, as the volume of publications increases. Darling is a web application, which utilizes Name Entity Recognition to identify human-related biomedical terms in PubMed articles, mentioned in OMIM, DisGeNET and Human Phenotype Ontology (HPO) disease records, and generates an interactive biomedical entity association network. Nodes in this network represent genes, proteins, chemicals, functions, tissues, diseases, environments and phenotypes. Users can search by identifiers, terms/entities or free text and explore the relevant abstracts in an annotated format. © 2022 by the authors. Licensee MDPI, Basel, Switzerland."
}