Omics technologies in mastitis: text mining and topic modelling analysis of global research trends

Mastitis, an inflammation of the mammary gland, is a disease of significant clinical and economic importance. In recent years, advances in omics technologies have provided powerful tools to unravel the complex biological mechanisms underlying mastitis. These approaches encompass diverse fields such as genomics, proteomics, transcriptomics, metagenomics, metabolomics, epigenomics, lipidomics, glycomics, pharmacogenomics, foodomics, interactomics and exposomics. However, despite the rapid growth of omics research, the thematic structure of this literature has not been systematically examined. In this study, latent dirichlet allocation (LDA) was employed to perform topic modelling on publications related to omics and mastitis retrieved from Scopus and Web of Science. The LDA analysis revealed ten distinct topics, labelled according to the most frequent terms within each cluster: 'proteomics', 'pathogen genomics', 'differential expression', 'metabolism', 'genetic selection', 'disease economy', 'molecular diagnostics', 'microbiome', 'antimicrobial resistance' and 'genetic variation.' Among these, the topics of 'genomics', 'differential expression' and 'antimicrobial resistance' accounted for the highest number of publications, while 'metabolism' emerged more recently. All topics exhibited an increasing trend in publication volume over time, likely driven by the declining costs and greater accessibility of high-throughput omics technologies. This study provides a comprehensive thematic overview of omics research on mastitis, identifies key areas of emphasis and emerging directions, and highlights knowledge gaps that may inform future investigations and the development of targeted strategies for disease control and prevention.