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Predicting Pseudomonas aeruginosa drug resistance using artificial intelligence and clinical MALDI-TOF mass spectra

Mendeley Data2026-04-09 收录

抗生素耐药性预测

病原菌AI诊断

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AST results for the article entitled "Predicting Pseudomonas aeruginosa drug resistance using artificial intelligence and clinical MALDI-TOF mass spectra".

应用场景：

抗生素耐药性预测

病原体基因组检测

Table S3. Significant predictive power of different subsets of resistance genes on antibiotic resistance genes not yet detected in pathogens (FARME database). (XLSX 11 kb)

Figshare2018-07-08 更新80

Figure 2. Comparison of ML Models in Predicting the Physicochemical Properties of Antibiotic Drugs Using R², MSE, MAE, and RMSE..docx

抗生素耐药性预测

机器学习模型比较

Comparison of ML Models in Predicting the Physicochemical Properties of Antibiotic Drugs

DataCite Commons2025-05-24 更新70

VAMPr: VAriant Mapping and Prediction of antibiotic resistance via explainable features and machine learning

抗生素耐药性预测

基因组生物信息学

Antimicrobial resistance (AMR) is an increasing threat to public health. Current methods of determining AMR rely on inefficient phenotypic approaches, and there remains incomplete understanding of AMR

Figshare2020-01-13 更新30

Supplementary Material 9

基因组序列聚类

抗生素耐药性预测

CD-HIT (Cluster Database at High Identity with Tolerance) is a widely used clustering algorithm that reduces redundancy in large genomic datasets. CD-HIT can group similar sequences or genomic feature

DataCite Commons2025-05-12 更新80

Table_2_β-LacFamPred: An online tool for prediction and classification of β-lactamase class, subclass, and family.xlsx

抗生素耐药性预测

β-内酰胺酶计算分类

β-Lactams are a broad class of antimicrobial agents with a high safety profile, making them the most widely used class in clinical, agricultural, and veterinary setups. The widespread use of β-lactams

NIAID Data Ecosystem40

Predicting Pseudomonas aeruginosa drug resistance using artificial intelligence and clinical MALDI-TOF mass spectra

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