首页|Analysis of high-molecular-weight proteins using MALDI-TOF MS and Machine Learni ng for the differentiation of clinically relevant Clostridioides difficile ribot ypes
Analysis of high-molecular-weight proteins using MALDI-TOF MS and Machine Learni ng for the differentiation of clinically relevant Clostridioides difficile ribot ypes
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews – According to news reporting based on a preprint a bstract, our journalists obtained the followingquote sourced from biorxiv.org:“Clostridioides difficile is the main cause of antibiotic related diarrhea and s ome ribotypes (RT), suchas RT027, RT181 or RT078, are considered high risk clon es.“A fast and reliable approach for C. difficile ribotyping is needed for a correc t clinical approach.“This study analyses high-molecular-weight proteins for C. difficile ribotyping with MALDI-TOF MS.Sixty-nine isolates representative of the most common ribotyp es in Europe were analyzed in the 17,000-65,000 m/z region and classified into 4 categories (RT027, RT181, RT078 and \’Other RTs\ ’). Fivesupervised Machine Learning algorithms were tested for this purpose: K- Nearest Neighbors, SupportVector Machine, Partial Least Squares-Discriminant An alysis, Random Forest and Light-Gradient BoostingMachine. All algorithms yielde d cross-validation results >70%, being RF a nd Light-GBM the bestperforming, with 88% of agreement. Area unde r the ROC curve of these two algorithms was >0.9.RT078 was correctly classified with 100% accuracy and isolates from the RT181 category could not bedifferentiated from RT027.”
NETL
NSTL
