Diane Duroux1,2,3,4, Paul P Meyer5, Giovanni Visoná6

  • 1ETH AI Center, ETH Zurich, Zurich, Switzerland.

GigaScience
|January 19, 2026
PubMed
概括

对于检测抗微生物药物耐药性的机器学习模型在普遍化方面存在困难. 使用光谱和化学数据的新深度学习方法改善了不同医院和不同时间段的模型性能.

相关概念视频

Constructing and Visualizing Models using Mime-based Machine-learning Framework06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Mime is a flexible computational framework to construct a machine learning-based integration model with elegant performance. Here, we provide a detailed step-by-step procedure for developing predictive models with high accuracy, leveraging complex datasets to identify critical genes associated with disease progression, patient outcomes, and therapeutic response.
2.3K
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

This study evaluates prognostic systems for colorectal signet-ring cell carcinoma patients using machine learning models and competing risk analyses. It identifies log odds of positive lymph nodes as a superior predictor compared to pN staging, demonstrating strong predictive performance and aiding clinical decision-making through robust survival prediction...
495
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

This protocol was designed to train a machine learning algorithm to use a combination of imaging parameters derived from magnetic resonance imaging (MRI) and positron emission tomography/computed tomography (PET/CT) in a rat model of breast cancer bone metastases to detect early metastatic disease and predict subsequent progression to...
7.4K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Intra-arterial therapies are the standard of care for patients with hepatocellular carcinoma who cannot undergo surgical resection. A method for predicting response to these therapies is proposed. The technique uses pre-procedural clinical, demographic, and imaging information to train machine learning models capable of predicting response prior to...
8.6K
Growing Mycobacterial Biofilm as a Model to Study Antimicrobial Resistance04:26

Growing Mycobacterial Biofilm as a Model to Study Antimicrobial Resistance

This protocol describes a robust method for developing pellicle biofilm. The method is scalable to different culture volumes, allowing easy adoption for various experimental objectives. The method's design enables qualitative or quantitative assessment of the biofilm-forming potential of several mycobacterial...
1.4K
Documentation in Long-Term and Home Healthcare Setting01:29

Documentation in Long-Term and Home Healthcare Setting

Documentation in long-term care facilities and home healthcare settings is crucial for ensuring continuous, coordinated, and comprehensive care for patients. Each setting has its specific documentation processes and tools:
Long-Term Care Facilities
1.4K