  Citation: Karatzas, E.; Baltoumas, F.A.; Kasionis, I.; Sanoudou, D.; Eliopoulos, A.G.; Theodosiou, T.; Iliopoulos, I.; Pavlopoulos, G.A. Darling: A Web Application for Detecting Disease-Related Biomedical Entity Associations with Literature Mining. Biomolecules 2022, 12, 520. https://doi.org/10.3390/ biom12040520 Academic Editor: Lukasz Kurgan Received: 1 March 2022 Accepted: 28 March 2022 Published: 30 March 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). biomolecules Article Darling: A Web Application for Detecting Disease-Related Biomedical Entity Associations with Literature Mining Evangelos Karatzas 1, * ,† , Fotis A. Baltoumas 1, * ,† , Ioannis Kasionis 1,† , Despina Sanoudou 2,3,4 , Aristides G. Eliopoulos 3,4,5 , Theodosios Theodosiou 6 , Ioannis Iliopoulos 6 and Georgios A. Pavlopoulos 1,3, * 1 Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center “Alexander Fleming”, 16672 Vari, Greece; gkasionis2@gmail.com 2 Clinical Genomics and Pharmacogenomics Unit, 4th Department of Internal Medicine, School of Medicine, National and Kapodistrian University of Athens, 11527 Athens, Greece; dsanoudou@bioacademy.gr 3 Center for New Biotechnologies and Precision Medicine, School of Medicine, National and Kapodistrian University of Athens, 11527 Athens, Greece; eliopag@med.uoa.gr 4 Biomedical Research Foundation of the Academy of Athens, 4 Soranou Ephessiou Street, 11527 Athens, Greece 5 Department of Biology, School of Medicine, National and Kapodistrian University of Athens, Mikras Asias 75, 11527 Athens, Greece 6 Department of Basic Sciences, School of Medicine, University of Crete, 71003 Heraklion, Greece; theodosios.theodosiou@gmail.com (T.T.); iliopj@med.uoc.gr (I.I.) * Correspondence: karatzas@fleming.gr (E.K.); baltoumas@fleming.gr (F.A.B.); pavlopoulos@fleming.gr (G.A.P.) † These authors contributed equally to this work. 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. Keywords: text-mining; data integration; bioinformatics; named-entity recognition; literature- derived associations 1. Introduction PubMed ® today (02/2022) hosts more than 33 million biomedical abstracts, whereas PubMed Central ® Open Access Subset (PMC OA Subset) [1] contains more than 7 Million full-text articles. The ever-increasing amount of literature is posing numer- ous challenges for bioscientists, as parsing these texts and extracting associations among biomedical entities is neither easy nor trivial. This is particularly true for disease-related research, where a wealth of knowledge on the relations between bioentities (genes, proteins, chemicals, etc.) and pathological conditions is available, especially since the rise of high- throughput experimental methods [2]. There is, therefore, a great need for the development of effective and user-friendly methods for the automated recognition, visualization and analysis of disease-related bioentity associations. Towards this end, several text-mining approaches have been implemented [3–7]. Bio- TextQuest [8], for example, retrieves PubMed articles and clusters them based on their biomedical terms. DrugQuest [9] applies text mining on the DrugBank database [10], in order to explore drug associations. DISEASES [11] is a system for extracting disease–gene associations from biomedical abstracts. PREGO [12] uses text mining to link microor- ganisms with environmental processes and functions. Reflect [13] and EXTRACT [14] Biomolecules 2022, 12, 520. https://doi.org/10.3390/biom12040520 https://www.mdpi.com/journal/biomolecules