{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T18:16:23Z","timestamp":1787940983312,"version":"build-2784847793"},"reference-count":58,"publisher":"Oxford University Press (OUP)","issue":"8","license":[{"start":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T00:00:00Z","timestamp":1784678400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"HelmholtzAI project AMR-XAI"},{"DOI":"10.13039\/501100002347","name":"BMBF","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002347","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,8,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Summary<\/jats:title>\n                    <jats:p>The field of machine learning in antimicrobial resistance (AMR) research has experienced rapid growth, fueled by advancements in high-throughput genome sequencing and the growing capacity of computational resources. However, the complexity and lack of standardized data preparation and bioinformatic analyses present significant challenges, especially for newcomers to the domain. In response to these challenges, we introduce ALPAR (Automated Learning Pipeline for Antimicrobial Resistance), a comprehensive AMR data analysis tool covering the entire process from processing of raw genomic data to training machine learning models to interpretation of results. Our method relies on a reproducible pipeline that integrates widely used bioinformatics tools, presenting a simplified, automatic workflow specifically tailored for single-reference AMR analysis. Accepting genomic data in the form of FASTA files as input, ALPAR facilitates the generation of machine learning-ready data tables and both the training of machine learning and the execution of genome-wide association studies (GWAS) experiments. Additionally, our tool offers supplementary functionalities such as phylogeny-based analysis of the distribution of mutations, enhancing its utility for researchers. The tool has also proven its performance in competitive benchmarks, winning the 2024 CAMDA Anti-Microbial Resistance Prediction Challenge and placing third in the 2025 edition.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>ALPAR is open-source and freely accessible via GitHub (https:\/\/github.com\/kalininalab\/ALPAR). The pipeline is fully reproducible and can be easily installed as a Conda package (https:\/\/anaconda.org\/kalininalab\/ALPAR)<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btag545","type":"journal-article","created":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T11:34:54Z","timestamp":1784633694000},"source":"Crossref","is-referenced-by-count":0,"title":["ALPAR: automated learning pipeline for antimicrobial resistance"],"prefix":"10.1093","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-8133-9403","authenticated-orcid":false,"given":"Alper","family":"Yurtseven","sequence":"first","affiliation":[{"name":"Department of Drug Bioinformatics, Helmholtz Institute for Pharmaceutical Research Saarland (HIPS), Helmholtz Centre for Infection Research (HZI) , Saarbr\u00fccken, Saarland 66123,","place":["Germany"]},{"name":"Center for Bioinformatics, Saarland University , Saarbr\u00fccken, Saarland 66123,","place":["Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-2952-9395","authenticated-orcid":false,"given":"Roman","family":"Joeres","sequence":"additional","affiliation":[{"name":"Department of Drug Bioinformatics, Helmholtz Institute for Pharmaceutical Research Saarland (HIPS), Helmholtz Centre for Infection Research (HZI) , Saarbr\u00fccken, Saarland 66123,","place":["Germany"]},{"name":"Center for Bioinformatics, Saarland University , Saarbr\u00fccken, Saarland 66123,","place":["Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9445-477X","authenticated-orcid":false,"given":"Olga V","family":"Kalinina","sequence":"additional","affiliation":[{"name":"Department of Drug Bioinformatics, Helmholtz Institute for Pharmaceutical Research Saarland (HIPS), Helmholtz Centre for Infection Research (HZI) , Saarbr\u00fccken, Saarland 66123,","place":["Germany"]},{"name":"Center for Bioinformatics, Saarland University , Saarbr\u00fccken, Saarland 66123,","place":["Germany"]},{"name":"Faculty of Medicine, Saarland University , Saarland 66421,","place":["Germany"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,7,22]]},"reference":[{"key":"2026082813192719000_btag545-B1","doi-asserted-by":"publisher","first-page":"e0126020","DOI":"10.1128\/JCM.01260-20","article-title":"Applications of machine learning to the problem of antimicrobial resistance: an emerging model for translational research","volume":"59","author":"Anahtar","year":"2021","journal-title":"J Clin Microbiol"},{"key":"2026082813192719000_btag545-B2","doi-asserted-by":"publisher","first-page":"e0319460","DOI":"10.1371\/journal.pone.0319460","article-title":"Machine learning for predicting antimicrobial resistance in critical and high-priority pathogens: a systematic review considering antimicrobial susceptibility tests in real-world healthcare settings","volume":"20","author":"Ardila","year":"2025","journal-title":"PLoS One"},{"key":"2026082813192719000_btag545-B3","doi-asserted-by":"publisher","DOI":"10.7554\/elife.05826","article-title":"The unexhausted potential of E. coli","volume":"4","author":"Blount","year":"2015","journal-title":"Elife"},{"key":"2026082813192719000_btag545-B4","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/a:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach Learn"},{"key":"2026082813192719000_btag545-B5","author":"CAMDA Organizing Committee"},{"key":"2026082813192719000_btag545-B6","author":"CAMDA Organizing Committee"},{"key":"2026082813192719000_btag545-B7","volume-title":"Antibiotic Resistance Threats in the United States","author":"CDC","year":"2019"},{"key":"2026082813192719000_btag545-B8","doi-asserted-by":"publisher","author":"Chindelevitch","year":"2022","DOI":"10.48550\/arxiv.2208.04683"},{"key":"2026082813192719000_btag545-B9","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1016\/j.mib.2015.03.002","article-title":"The advent of genome-wide association studies for bacteria","volume":"25","author":"Chen","year":"2015","journal-title":"Curr Opin Microbiol"},{"key":"2026082813192719000_btag545-B10","doi-asserted-by":"publisher","first-page":"e1004547","DOI":"10.1371\/journal.pgen.1004547","article-title":"Comprehensive identification of single nucleotide polymorphisms associated with beta-lactam resistance within pneumococcal mosaic genes","volume":"10","author":"Chewapreecha","year":"2014","journal-title":"PLoS Genet"},{"key":"2026082813192719000_btag545-B11","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1186\/s13059-021-02473-1","article-title":"Pandora: nucleotide-resolution bacterial pan-genomics with reference graphs","volume":"22","author":"Colquhoun","year":"2021","journal-title":"Genome Biol"},{"key":"2026082813192719000_btag545-B12","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1023\/A:1022627411411","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach Learn"},{"key":"2026082813192719000_btag545-B13","doi-asserted-by":"publisher","DOI":"10.1101\/2025.11.12.688105","article-title":"A comprehensive AMR genotype-phenotype database (cabbage)","author":"Dickens","year":"2025"},{"key":"2026082813192719000_btag545-B14","doi-asserted-by":"publisher","first-page":"856","DOI":"10.1038\/s42003-022-03812-z","article-title":"Non-linear machine learning models incorporating snps and prs improve polygenic prediction in diverse human populations","volume":"5","author":"Elgart","year":"2022","journal-title":"Commun Biol"},{"key":"2026082813192719000_btag545-B15","doi-asserted-by":"publisher","first-page":"621","DOI":"10.1164\/rccm.201510-2091oc","article-title":"Genetic determinants of drug resistance in Mycobacterium tuberculosis and their diagnostic value","volume":"194","author":"Farhat","year":"2016","journal-title":"Am J Respir Crit Care Med"},{"key":"2026082813192719000_btag545-B16","doi-asserted-by":"publisher","first-page":"W463","DOI":"10.1093\/nar\/gkw364","article-title":"StructMAn: annotation of single-nucleotide polymorphisms in the structural context","volume":"44","author":"Gress","year":"2016","journal-title":"Nucleic Acids Res"},{"key":"2026082813192719000_btag545-B17","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1016\/S0140-6736(15)00473-0","article-title":"Understanding the mechanisms and drivers of antimicrobial resistance","volume":"387","author":"Holmes","year":"2016","journal-title":"Lancet"},{"key":"2026082813192719000_btag545-B18","doi-asserted-by":"publisher","first-page":"btaf206","DOI":"10.1093\/bioinformatics\/btaf206","article-title":"Whole-genome phenotype prediction with machine learning: open problems in bacterial genomics","volume":"41","author":"James","year":"2025","journal-title":"Bioinformatics"},{"key":"2026082813192719000_btag545-B19","doi-asserted-by":"crossref","first-page":"3337","DOI":"10.1038\/s41467-025-58606-8","article-title":"Data splitting to avoid information leakage with datasail","volume":"16","author":"Joeres","year":"2025","journal-title":"Nat Commun"},{"key":"2026082813192719000_btag545-B20","doi-asserted-by":"publisher","first-page":"1762","DOI":"10.21105\/joss.01762","article-title":"Mashtree: a rapid comparison of whole genome sequence files","volume":"4","author":"Katz","year":"2019","journal-title":"J Open Source Softw"},{"key":"2026082813192719000_btag545-B21","doi-asserted-by":"publisher","first-page":"e10264","DOI":"10.15252\/emmm.201910264","article-title":"Predicting antimicrobial resistance in Pseudomonas aeruginosa with machine learning-enabled molecular diagnostics","volume":"12","author":"Khaledi","year":"2020","journal-title":"EMBO Mol Med"},{"key":"2026082813192719000_btag545-B22","doi-asserted-by":"publisher","first-page":"e0017921","DOI":"10.1128\/cmr.00179-21","article-title":"Machine learning for antimicrobial resistance prediction: current practice, limitations, and clinical perspective","volume":"35","author":"Kim","year":"2022","journal-title":"Clin Microbiol Rev"},{"key":"2026082813192719000_btag545-B23","doi-asserted-by":"publisher","first-page":"2402","DOI":"10.1038\/s41467\u201302672225\u2013x","article-title":"Convolutional neural networks quantify antibiotic resistance in Mycobacterium tuberculosis with diagnostic grade accuracy and predict treatment response","volume":"17","author":"Kulkarni","year":"2026","journal-title":"Nat Communnature Communications"},{"key":"2026082813192719000_btag545-B24","doi-asserted-by":"publisher","first-page":"S28","DOI":"10.1093\/infdis\/jiw282","article-title":"The epidemiology of carbapenem-resistant Enterobacteriaceae: the impact and evolution of a global menace","volume":"215","author":"Logan","year":"2017","journal-title":"J Infect Dis"},{"key":"2026082813192719000_btag545-B25","doi-asserted-by":"publisher","first-page":"1276332","DOI":"10.3389\/fmicb.2023.1276332","article-title":"Word-based GWAS harnesses the rich potential of genomic data for E. coli quinolone resistance","volume":"14","author":"Malekian","year":"2023","journal-title":"Front Microbiol"},{"key":"2026082813192719000_btag545-B26","doi-asserted-by":"publisher","first-page":"e1005944","DOI":"10.1371\/journal.pcbi.1005944","article-title":"MUMmer4: a fast and versatile genome alignment system","volume":"14","author":"Mar\u00e7ais","year":"2018","journal-title":"PLoS Comput Biol"},{"key":"2026082813192719000_btag545-B27","doi-asserted-by":"publisher","first-page":"3348","DOI":"10.1128\/aac.00419-13","article-title":"The comprehensive antibiotic resistance database","volume":"57","author":"McArthur","year":"2013","journal-title":"Antimicrob Agents Chemother"},{"key":"2026082813192719000_btag545-B28","doi-asserted-by":"publisher","first-page":"e1006258","DOI":"10.1371\/journal.pcbi.1006258","article-title":"Prediction of antibiotic resistance in Escherichia coli from large-scale pan-genome data","volume":"14","author":"Moradigaravand","year":"2018","journal-title":"PLoS Comput Biol"},{"key":"2026082813192719000_btag545-B29","doi-asserted-by":"publisher","first-page":"2866","DOI":"10.3390\/microorganisms11122866","article-title":"Genome-wide association studies (GWAS) approaches for the detection of genetic variants associated with antibiotic resistance: a systematic review","volume":"11","author":"Mosquera-Rendon","year":"2023","journal-title":"Microorganisms"},{"key":"2026082813192719000_btag545-B30","doi-asserted-by":"publisher","first-page":"e01591\u201318","DOI":"10.1128\/JCM.01591-18","article-title":"Using machine learning to predict antimicrobial mics and associated genomic features for nontyphoidal Salmonella","volume":"57","author":"Nguyen","year":"2019","journal-title":"J Clin Microbiol"},{"key":"2026082813192719000_btag545-B31"},{"key":"2026082813192719000_btag545-B32","doi-asserted-by":"publisher","first-page":"279","DOI":"10.3390\/diagnostics15030279","article-title":"Predicting drug resistance in mycobacterium tuberculosis: a machine learning approach to genomic mutation analysis","volume":"15","author":"Paredes-Gutierrez","year":"2025","journal-title":"Diagnostics (Basel)"},{"key":"2026082813192719000_btag545-B33","doi-asserted-by":"publisher","first-page":"lqaa106","DOI":"10.1093\/nargab\/lqaa106","article-title":"PanACoTA: a modular tool for massive microbial comparative genomics","volume":"3","author":"Perrin","year":"2021","journal-title":"NAR Genom Bioinform"},{"key":"2026082813192719000_btag545-B34","doi-asserted-by":"publisher","first-page":"1887","DOI":"10.3389\/fmicb.2016.01887","article-title":"Evaluation of machine learning and rules-based approaches for predicting antimicrobial resistance profiles in gram-negative bacilli from whole genome sequence data","volume":"7","author":"Pesesky","year":"2016","journal-title":"Front Microbiol"},{"key":"2026082813192719000_btag545-B35","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1038\/nrg.2016.132","article-title":"Microbial genome-wide association studies: lessons from human GWAS","volume":"18","author":"Power","year":"2017","journal-title":"Nat Rev Genet"},{"key":"2026082813192719000_btag545-B36","doi-asserted-by":"publisher","first-page":"e102","DOI":"10.1002\/cpbi.102","article-title":"Using SPAdes de novo assembler","volume":"70","author":"Prjibelski","year":"2020","journal-title":"Curr Protoc Bioinformatics"},{"key":"2026082813192719000_btag545-B37","doi-asserted-by":"publisher","first-page":"203","DOI":"10.2174\/1874285801711010203","article-title":"Genetic investigation of beta-lactam associated antibiotic resistance among Escherichia coli strains isolated from water sources","volume":"11","author":"Ranjbar","year":"2017","journal-title":"Open Microbiol J"},{"key":"2026082813192719000_btag545-B38","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1186\/s13073-014-0109-z","article-title":"Characterizing the genetic basis of bacterial phenotypes using genome-wide association studies: a new direction for bacteriology","volume":"6","author":"Read","year":"2014","journal-title":"Genome Med"},{"key":"2026082813192719000_btag545-B39","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1093\/bioinformatics\/btab681","article-title":"Prediction of antimicrobial resistance based on whole-genome sequencing and machine learning","volume":"38","author":"Ren","year":"2022","journal-title":"Bioinformatics"},{"key":"2026082813192719000_btag545-B40","doi-asserted-by":"publisher","first-page":"452","DOI":"10.3390\/antibiotics12030452","article-title":"Using machine learning to predict antimicrobial resistance\u2015a literature review","volume":"12","author":"Sakagianni","year":"2023","journal-title":"Antibiotics"},{"key":"2026082813192719000_btag545-B41","doi-asserted-by":"publisher","first-page":"1052","DOI":"10.3390\/antibiotics13111052","article-title":"Data-driven approaches in antimicrobial resistance: machine learning solutions","volume":"13","author":"Sakagianni","year":"2024","journal-title":"Antibiotics (Basel)"},{"key":"2026082813192719000_btag545-B42","doi-asserted-by":"publisher","first-page":"000685","DOI":"10.1099\/mgen.0.000685","article-title":"Bakta: rapid and standardized annotation of bacterial genomes via alignment-free sequence identification","volume":"7","author":"Schwengers","year":"2021","journal-title":"Microb Genom"},{"key":"2026082813192719000_btag545-B43","doi-asserted-by":"publisher","first-page":"2068","DOI":"10.1093\/bioinformatics\/btu153","article-title":"Prokka: rapid prokaryotic genome annotation","volume":"30","author":"Seemann","year":"2014","journal-title":"Bioinformatics"},{"key":"2026082813192719000_btag545-B44","author":"Seemann","year":"2015"},{"key":"2026082813192719000_btag545-B45","doi-asserted-by":"publisher","first-page":"001129","DOI":"10.1099\/mgen.0.001129","article-title":"Reduced ambiguity and improved interpretability of bacterial genome-wide associations using gene-cluster-centric k-mers","volume":"9","author":"Sommer","year":"2023","journal-title":"Microb Genom"},{"key":"2026082813192719000_btag545-B46","doi-asserted-by":"publisher","first-page":"106684","DOI":"10.1016\/j.ijantimicag.2022.106684","article-title":"Machine learning in predicting antimicrobial resistance: a systematic review and meta-analysis","volume":"60","author":"Tang","year":"2022","journal-title":"Int J Antimicrob Agents"},{"key":"2026082813192719000_btag545-B47","doi-asserted-by":"publisher","first-page":"180","DOI":"10.1186\/s13059-020-02090-4","article-title":"Producing polished prokaryotic pangenomes with the Panaroo pipeline","volume":"21","author":"Tonkin-Hill","year":"2020","journal-title":"Genome Biol"},{"key":"2026082813192719000_btag545-B48","first-page":"277","article-title":"The antibiotic resistance crisis: part 1: causes and threats","volume":"40","author":"Ventola","year":"2015","journal-title":"P T"},{"key":"2026082813192719000_btag545-B49","doi-asserted-by":"publisher","first-page":"D535","DOI":"10.1093\/nar\/gkw1017","article-title":"Improvements to patric, the all-bacterial bioinformatics database and analysis resource center","volume":"45","author":"Wattam","year":"2017","journal-title":"Nucleic Acids Res"},{"key":"2026082813192719000_btag545-B50","doi-asserted-by":"publisher","first-page":"686197","DOI":"10.3389\/fmicb.2021.639660","article-title":"Genome-wide association studies for the detection of genetic variants associated with Daptomycin and Ceftaroline resistance in Staphylococcus aureus","volume":"12","author":"Weber","year":"2021","journal-title":"Front Microbiol"},{"key":"2026082813192719000_btag545-B51"},{"key":"2026082813192719000_btag545-B52","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12859-022-04666-2","article-title":"Enhancing predictions of antimicrobial resistance of pathogens by expanding the potential resistance gene repertoire using a pan-genome-based feature selection approach","volume":"23","author":"Wu","year":"2022","journal-title":"BMC Bioinformatics"},{"key":"2026082813192719000_btag545-B53","doi-asserted-by":"publisher","first-page":"3240","DOI":"10.1093\/bioinformatics\/btz067","article-title":"Deepamr for predicting co-occurrent resistance of Mycobacterium tuberculosis","volume":"35","author":"Yang","year":"2019","journal-title":"Bioinformatics"},{"key":"2026082813192719000_btag545-B54","doi-asserted-by":"publisher","first-page":"e3003539","DOI":"10.1371\/journal.pbio.3003539","article-title":"Biased sampling driven by bacterial population structure confounds machine learning prediction of antimicrobial resistance","volume":"23","author":"Yu","year":"2025","journal-title":"PLoS Biol"},{"key":"2026082813192719000_btag545-B55","doi-asserted-by":"publisher","first-page":"404","DOI":"10.1186\/s12866-023-03147-7","article-title":"Machine learning and phylogenetic analysis allow for predicting antibiotic resistance in M. tuberculosis","volume":"23","author":"Yurtseven","year":"2023","journal-title":"BMC Microbiol"},{"key":"2026082813192719000_btag545-B56","doi-asserted-by":"publisher","first-page":"W528","DOI":"10.1093\/nar\/gkaf381","article-title":"Structman 2.0 web: a web server for structural annotation of protein sequences and mutations","volume":"53","author":"Yurtseven","year":"2025","journal-title":"Nucleic Acids Res"},{"key":"2026082813192719000_btag545-B57","doi-asserted-by":"publisher","first-page":"2640","DOI":"10.1093\/jac\/dks261","article-title":"Identification of acquired antimicrobial resistance genes","volume":"67","author":"Zankari","year":"2012","journal-title":"J Antimicrob Chemother"},{"key":"2026082813192719000_btag545-B58","doi-asserted-by":"publisher","first-page":"1528696","DOI":"10.3389\/fmicb.2025.1528696","article-title":"Integrating sequencing methods with machine learning for antimicrobial susceptibility testing in pediatric infections: current advances and future insights","volume":"16","author":"Zou","year":"2025","journal-title":"Front Microbiol"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/advance-article-pdf\/doi\/10.1093\/bioinformatics\/btag545\/69799130\/btag545.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/42\/8\/btag545\/69799130\/btag545.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/42\/8\/btag545\/69799130\/btag545.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T17:19:34Z","timestamp":1787937574000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/doi\/10.1093\/bioinformatics\/btag545\/8739913"}},"subtitle":[],"editor":[{"given":"Xin","family":"Gao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2026,7,22]]},"references-count":58,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2026,8,3]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btag545","relation":{},"ISSN":["1367-4803","1367-4811"],"issn-type":[{"value":"1367-4803","type":"print"},{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2026,8]]},"published":{"date-parts":[[2026,7,22]]},"article-number":"btag545"}}