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However, the challenge still exists to predict fully minimum inhibitory concentrations (MICs) and antimicrobial susceptibility phenotypes based on WGS data. This study aimed to establish an artificial intelligence-based computational approach in predicting antimicrobial susceptibilities of multidrug-resistant\n                      <jats:italic>Acinetobacter baumannii<\/jats:italic>\n                      from WGS and gene expression data. Antimicrobial susceptibility testing (AST) was performed using the broth microdilution method for 10 antimicrobial agents.\n                      <jats:italic>In silico<\/jats:italic>\n                      multilocus sequence typing (MLST), antimicrobial resistance genes, and phylogeny based on cgSNP and cgMLST strategies were analyzed. High-throughput qPCR was performed to measure the expression level of antimicrobial resistance (AMR) genes. Most isolates exhibited a high level of resistance to most of the tested antimicrobial agents, with the majority belonging to the IC2\/CC92 lineage. Phylogenetic analysis revealed undetected transmission events or local outbreaks. The percentage agreements between AMR phenotype and genotype ranged from 70.08% to 89.96%, with the coefficient of agreement (\u03ba) extending from 0.025 and 0.881. The prediction of AST employed by deep neural network models achieved an accuracy of up to 98.64% on the testing data set. Additionally, several linear regression models demonstrated high prediction accuracy, reaching up to 86.15% within an error range of one gradient, indicating a linear relationship between certain gene expressions and the corresponding antimicrobial MICs. In conclusion, neural network-based predictions could be used as a tool for the surveillance of antimicrobial resistance in multidrug-resistant\n                      <jats:italic>A. baumannii<\/jats:italic>\n                      .\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1128\/aac.01446-24","type":"journal-article","created":{"date-parts":[[2024,11,14]],"date-time":"2024-11-14T09:02:08Z","timestamp":1731574928000},"update-policy":"https:\/\/doi.org\/10.1128\/asmj-crossmark-policy-page","source":"Crossref","is-referenced-by-count":29,"title":["Neural network-based predictions of antimicrobial resistance phenotypes in multidrug-resistant\n                    <i>Acinetobacter baumannii<\/i>\n                    from whole genome sequencing and gene expression"],"prefix":"10.1128","volume":"68","author":[{"given":"Huiqiong","family":"Jia","sequence":"first","affiliation":[{"name":"Department of Laboratory Medicine, The First Affiliated Hospital, Zhejiang 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