[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120951-en":3,"doc-seo-120951-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},120951,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"Research & Report","Combining machine learning with high-content imaging to infer ciprofloxacin susceptibility in clinical isolates of Salmonella Typhimurium","Machine learning is combined with high-content imaging to infer ciprofloxacin susceptibility in clinical isolates of Salmonella Typhimurium. The material describes time- and concentration-dependent changes in bacterial morphology and length under ciprofloxacin exposure, using multiple fluorescence stains to capture cellular phenotypes. A random forest approach is evaluated through out-of-bag error analysis and feature importance ranking. Classifier performance is further benchmarked across training, validation, and test sets using accuracy, sensitivity, specificity, precision, F1 score, and AUC.","Combining machine learning with high-content imaging to infer ciprofloxacin susceptibility in clinical isolates of Salmonella Typhimurium  \n|  |  |  |  |  |  |  |\n| --- | --- | --- | --- | --- | --- | --- |\n\na  \nb  \nD23580  \n\n| Bacteria Length (µm) | 10\u003Cbr>8\u003Cbr>6\u003Cbr>4\u003Cbr>2\u003Cbr>10\u003Cbr>8\u003Cbr>6\u003Cbr>4\u003Cbr>2 | D23580 SL1344 \u003Cbr> SL1344gyrA VNS20081 \u003Cbr>\u003Cbr>5 10 15 20 25 5 10 15 20 25 |\n| --- | --- | --- |\n| Time [hour] |  |  |\n\n2 6 10 14 18 22 24  \nTime [hour]  \nc  \nV NS20081  \n\n|  | \u003Cbr>6 | \u003Cbr>10 | \u003Cbr>14 | \u003Cbr>18 | \u003Cbr>22 | \u003Cbr>24 |\n| --- | --- | --- | --- | --- | --- | --- |\n\nConcentration  \n0x  \n1x  \n2x  \n4x  \nConcentration  \n0x  \n1x  \n2x  \n4x  \nTime [hour]  \nSupplementary Fig. 2. Change in bacterial length over time and ciprofloxacin exposure.  \n(a) Dynamics of bacterial length (vertical axis) over 24h of ciprofloxacin exposure (horizontal axis) . (b) and (c) Morphologies of  \nS. Typhimurium D23580 and VNS20081, respectively, at different ciprofloxacin relative concentration and exposure time. Relative concentrations (x MIC) increase from top to bottom and exposure time increases from left to right. Red, green, and blue fluorescence are from CSA, SYTOX Green, and DAPI stains, respectively.  \na  \n\n| Error Rate | 0.08\u003Cbr>0.06\u003Cbr>0.04\u003Cbr>0.02\u003Cbr>0.00 | \u003Cbr>Number of Trees |\n| --- | --- | --- |\n\nb  \nMean  \nDAPI Intensity  \nSG Profile 2.2  \nCSA Profile 2.2  \nSG Radial Relative Deviation  \n Susceptible  \nSG Intensity Mean  Resistant  \nSG Threshold Compactness 50  \nSG Symmetry 15  \nCSA Profile 1.2  \nSG Profile 1.2  \nDAPI Intensity StdDev  \nSupplementary Fig. 4. Random forest model differentiation of resistant and susceptible isolates at 0xMIC-22h.  \n(a) OOB error rates of random forest models trained from data at 0xMIC-22h. Horizontal axis is number of decision trees used in random forest ensembles, and vertical axis is respective OOB error rate. (b) Spider chart for the ten most important features to differentiate susceptible (yellow) and resistant (dark purple) isolates. Data was transferred to z-score, and each line is a datapoint.  \nD23580  \nVNS20081 SL1344gyrA  \nSL1344  \n13 other isolates  \nSupplementary Fig. 5. Isolates evaluated in machine learning classifier testing, training, and validation sets.  \nVenn diagram of isolates used to train machine learning classifiers. Green circle represents the four main isolates and orange circle represents 16 isolates to test the generalization of the classifiers.  \nSupplementary Table 1. Evaluation of different machine learning methods to identify resistant S. Typhimurium on training, validation, and test sets.  \nTraining set Validation set Test set  \n\n| Method | Accura\u003Cbr>cy | Sensiti\u003Cbr>vity | Specifi\u003Cbr>city | Precisi\u003Cbr>on | F1\u003Cbr>score AUC |  | Accura\u003Cbr>cy | Sensiti\u003Cbr>vity | Specifi\u003Cbr>city | Precisi\u003Cbr>on | F1\u003Cbr>score AUC |  | Accura\u003Cbr>cy | Sensiti\u003Cbr>vity | Specifi\u003Cbr>city | Precisi\u003Cbr>on | F1\u003Cbr>score AUC |  |\n| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n| Naïve\u003Cbr>Bayes\u003Cbr>KNN\u003Cbr>SVM Random forest\u003Cbr>CatBoost\u003Cbr>Neural\u003Cbr>network | 0.79±0\u003Cbr>.06\u003Cbr>0.86±0\u003Cbr>.04\u003Cbr>0.87±0\u003Cbr>.04\u003Cbr>0.71±0\u003Cbr>.06\u003Cbr>0.64±0\u003Cbr>.06\u003Cbr>0.83±0\u003Cbr>.06 | 0.64±0\u003Cbr>.12\u003Cbr>0.79±0\u003Cbr>.06\u003Cbr>0.84±0\u003Cbr>.07\u003Cbr>0.99±0\u003Cbr>.02\u003Cbr>1.00±0\u003Cbr>.01\u003Cbr>0.83±0\u003Cbr>.12 | 0.93±0\u003Cbr>.04\u003Cbr>0.92±0\u003Cbr>.04\u003Cbr>0.91±0\u003Cbr>.05\u003Cbr>0.45±0\u003Cbr>.11\u003Cbr>0.31±0\u003Cbr>.11\u003Cbr>0.83±0\u003Cbr>.10 | 0.89±0\u003Cbr>.07\u003Cbr>0.90±0\u003Cbr>.05\u003Cbr>0.89±0\u003Cbr>.06\u003Cbr>0.62±0\u003Cbr>.06\u003Cbr>0.57±0\u003Cbr>.06\u003Cbr>0.82±0\u003Cbr>.08 | 0.74±0\u003Cbr>.10\u003Cbr>0.84±0\u003Cbr>.05\u003Cbr>0.86±0\u003Cbr>.05\u003Cbr>0.76±0\u003Cbr>.04\u003Cbr>0.73±0\u003Cbr>.04\u003Cbr>0.82±0\u003Cbr>.07 | 0.90±0\u003Cbr>.05\u003Cbr>0.91±0\u003Cbr>.05\u003Cbr>0.92±0\u003Cbr>.05\u003Cbr>0.92±0\u003Cbr>.04\u003Cbr>0.87±0\u003Cbr>.05\u003Cbr>0.91±0\u003Cbr>.04 | 0.73±0\u003Cbr>.13\u003Cbr>0.80±0\u003Cbr>.11\u003Cbr>0.80±0\u003Cbr>.09\u003Cbr>0.73±0\u003Cbr>.10\u003Cbr>0.68±0\u003Cbr>.09\u003Cbr>0.88±0\u003Cbr>.08 | 0.61±0\u003Cbr>.25\u003Cbr>0.73±0\u003Cbr>.20\u003Cbr>0.77±0\u003Cbr>.15\u003Cbr>0.99±0\u003Cbr>.03\u003Cbr>1.00±0\u003Cbr>.02\u003Cbr>0.88±0\u003Cbr>.11 | 0.86±0\u003Cbr>.14\u003Cbr>0.87±0\u003Cbr>.11\u003Cbr>0.84±0\u003Cbr>.12\u003Cbr>0.45±0\u003Cbr>.19\u003Cbr>0.32±0\u003Cbr>.17\u003Cbr>0.8","cbCaiqJuInNfDoPJ","https://ap.wps.com/l/cbCaiqJuInNfDoPJ","pdf",4562051,1,"English","en",105,"# Supplementary Figures\n## Bacterial length and morphology under ciprofloxacin exposure\n## Random forest differentiation of resistant and susceptible isolates\n## Isolates used for classifier training, validation, and testing\n# Supplementary Tables\n## Evaluation of multiple machine learning methods\n## Isolates and whole-genome sequencing accession numbers","[{\"question\":\"How is ciprofloxacin susceptibility assessed using high-content imaging in the study?\",\"answer\":\"The study tracks bacterial morphology and length over 24 hours under varying ciprofloxacin relative concentrations, quantified using fluorescence stains. These imaging-derived features are then used as inputs to machine learning classifiers.\"},{\"question\":\"What does the random forest evaluation show for resistant versus susceptible isolates?\",\"answer\":\"It reports out-of-bag (OOB) error rates across different numbers of decision trees and provides a spider chart of the ten most important discriminative features separating susceptible and resistant isolates.\"},{\"question\":\"Which machine learning methods are compared, and how is performance measured?\",\"answer\":\"Multiple methods are compared, including Naïve Bayes, KNN, SVM, random forest, CatBoost, and neural networks. Performance is reported on training, validation, and test sets using metrics such as accuracy, sensitivity, specificity, precision, F1 score, and AUC.\"}]","Combining machine learning with high-content imaging to infer ciprofloxacin susceptibility in clinical isolates of Salmonella Typhimurium | PDF",1785733010,20,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"combining-machine-learning-with-high-content-imaging-to-infer-ciprofloxacin-susceptibility-in-clinical-isolates-of-salmonella-typhimurium","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/combining-machine-learning-with-high-content-imaging-to-infer-ciprofloxacin-susceptibility-in-clinical-isolates-of-salmonella-typhimurium/120951/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":22,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"How is ciprofloxacin susceptibility assessed using high-content imaging in the study?","Question",{"text":74,"@type":75},"The study tracks bacterial morphology and length over 24 hours under varying ciprofloxacin relative concentrations, quantified using fluorescence stains. These imaging-derived features are then used as inputs to machine learning classifiers.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"What does the random forest evaluation show for resistant versus susceptible isolates?",{"text":79,"@type":75},"It reports out-of-bag (OOB) error rates across different numbers of decision trees and provides a spider chart of the ten most important discriminative features separating susceptible and resistant isolates.",{"name":81,"@type":72,"acceptedAnswer":82},"Which machine learning methods are compared, and how is performance measured?",{"text":83,"@type":75},"Multiple methods are compared, including Naïve Bayes, KNN, SVM, random forest, CatBoost, and neural networks. Performance is reported on training, validation, and test sets using metrics such as accuracy, sensitivity, specificity, precision, F1 score, and AUC.","https://schema.org",{"og:url":51,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":51},"index,follow",{"doc_id":7,"site_id":23},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,119,122,126,129,133],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":45,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":45,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":45,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":45,"category_name":124,"show_sort_weight":28,"slug":125},9,"Religion & Spirituality","religion-spirituality",{"id":28,"doc_module":4,"doc_module_name":45,"category_name":127,"show_sort_weight":28,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":45,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":45,"category_name":135,"show_sort_weight":105,"slug":136},19,"General","general"]