Darling: A Web Application for Detecting Disease-Related Biomedical Entity Associations with Literature Mining
Evangelos Karatzas,
Fotis A. Baltoumas,
Ioannis Kasionis,
Despina Sanoudou,
Aristides G. Eliopoulos,
Theodosios Theodosiou,
Ioannis Iliopoulos,
Georgios A. Pavlopoulos
Affiliations
Evangelos Karatzas
Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center “Alexander Fleming”, 16672 Vari, Greece
Fotis A. Baltoumas
Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center “Alexander Fleming”, 16672 Vari, Greece
Ioannis Kasionis
Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center “Alexander Fleming”, 16672 Vari, Greece
Despina Sanoudou
Clinical Genomics and Pharmacogenomics Unit, 4th Department of Internal Medicine, School of Medicine, National and Kapodistrian University of Athens, 11527 Athens, Greece
Aristides G. Eliopoulos
Center for New Biotechnologies and Precision Medicine, School of Medicine, National and Kapodistrian University of Athens, 11527 Athens, Greece
Theodosios Theodosiou
Department of Basic Sciences, School of Medicine, University of Crete, 71003 Heraklion, Greece
Ioannis Iliopoulos
Department of Basic Sciences, School of Medicine, University of Crete, 71003 Heraklion, Greece
Georgios A. Pavlopoulos
Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center “Alexander Fleming”, 16672 Vari, Greece
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.
