Text mining for identification of biological entities related to antibiotic resistant organisms

• Published in: PeerJ (San Francisco, CA) Vol. 10; p. e13351
• Main Authors: Fortunato Costa, Kelle, Almeida Araújo, Fabrício, Morais, Jefferson, Lisboa Frances, Carlos Renato, Ramos, Rommel T. J.
• Format: Journal Article
• Language: English
• Published: United States PeerJ, Inc 05.05.2022; PeerJ Inc
• Subjects: Algorithms; Annotations; Annual reports; Anti-Infective Agents; Antibiotic resistance; Antibiotics; Antimicrobial agents; Antimicrobial resistance; Application programming interface; Artificial Intelligence; Bioinformatics; Biological literature; Classification; Computational Science; Data Mining - methods; Data Mining and Machine Learning; Drug resistance; Genes; Information retrieval; Keywords; Knowledge discovery; Machine Learning; Medical Subject Headings-MeSH; Microbiology; Natural language; Ontology; Proteins; Public health; Text mining; Text mining; Antimicrobial resistance; Biological literature; Machine learning

Antimicrobial resistance is a significant public health problem worldwide. In recent years, the scientific community has been intensifying efforts to combat this problem; many experiments have been developed, and many articles are published in this area. However, the growing volume of biological lit...