{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["6(1)"],"submitter":["Lu JJ"],"pubmed_abstract":["<h4>Motivation</h4><i>Klebsiella pneumoniae</i> is a highly virulent superbug with rising antibiotic resistance worldwide. While matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) has transformed microbial identification, its application to antimicrobial resistance prediction remains underexplored, particularly for large clinical cohorts. In this study, we developed machine-learning models with feature-level interpretability using MALDI-TOF MS data to rapidly predict resistance to ciprofloxacin (CIP), cefuroxime (CXM), and ceftriaxone (CRO) in <i>K. pneumoniae</i>.<h4>Results</h4>Using more than 28 000 isolates from two hospitals, the best-performing models reached an independent test accuracy of 0.7858, with sensitivity of 0.7289 and specificity of"],"journal":["Bioinformatics advances"],"pagination":["vbaf303"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12776345"],"repository":["biostudies-literature"],"pubmed_title":["AI-powered rapid detection of multidrug-resistant &lt;i&gt;Klebsiella pneumoniae&lt;/i&gt; with informative peaks of MALDI-TOF MS."],"pmcid":["PMC12776345"],"pubmed_authors":["Lu JJ","Chiang MC","Wu LC","Lee TY","Horng JT","Hsu JB","Chung CR","Tang Y","Wang HY"],"additional_accession":[]},"is_claimable":false,"name":"AI-powered rapid detection of multidrug-resistant &lt;i&gt;Klebsiella pneumoniae&lt;/i&gt; with informative peaks of MALDI-TOF MS.","description":"<h4>Motivation</h4><i>Klebsiella pneumoniae</i> is a highly virulent superbug with rising antibiotic resistance worldwide. While matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) has transformed microbial identification, its application to antimicrobial resistance prediction remains underexplored, particularly for large clinical cohorts. In this study, we developed machine-learning models with feature-level interpretability using MALDI-TOF MS data to rapidly predict resistance to ciprofloxacin (CIP), cefuroxime (CXM), and ceftriaxone (CRO) in <i>K. pneumoniae</i>.<h4>Results</h4>Using more than 28 000 isolates from two hospitals, the best-performing models reached an independent test accuracy of 0.7858, with sensitivity of 0.7289 and specificity of","dates":{"release":"2026-01-01T00:00:00Z","publication":"2026","modification":"2026-06-13T06:30:50.892Z","creation":"2026-06-13T03:12:42.133Z"},"accession":"S-EPMC12776345","cross_references":{"pubmed":["41510023"],"doi":["10.1093/bioadv/vbaf303"]}}