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
• ISSN: 2167-8359; 2167-8359
• DOI: 10.7717/peerj.13351

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 literature increases the difficulty of the biocuration process due to the cost and time required. Modern text mining tools with the adoption of artificial intelligence technology are helpful to assist in the evolution of research. In this article, we propose a text mining model capable of identifying and ranking prioritizing scientific articles in the context of antimicrobial resistance. We retrieved scientific articles from the PubMed database, adopted machine learning techniques to generate the vector representation of the retrieved scientific articles, and identified their similarity with the context. As a result of this process, we obtained a dataset labeled “Relevant” and “Irrelevant” and used this dataset to implement one supervised learning algorithm to classify new records. The model’s overall performance reached 90% accuracy and the f-measure (harmonic mean between the metrics) reached 82% accuracy for positive class and 93% for negative class, showing quality in the identification of scientific articles relevant to the context. The dataset, scripts and models are available at https://github.com/engbiopct/TextMiningAMR .