Results 231 to 240 of about 2,919,066 (271)

Deep Learning‐Assisted Coherent Raman Scattering Microscopy

Advanced Intelligent Discovery, EarlyView.
The analytical capabilities of coherent Raman scattering microscopy are augmented through deep learning integration. This synergistic paradigm improves fundamental performance via denoising, deconvolution, and hyperspectral unmixing. Concurrently, it enhances downstream image analysis including subcellular localization, virtual staining, and clinical ...
Jianlin Liu, Rujia Liu, Xinyan Zhang, Xun Chen, Shuhua Yue   +4 more
wiley   +1 more source

Identification of Exhaled Volatile Organic Compounds Biomarkers for Lung Cancer Under Data‐Limited Conditions Using Data Augmentation and Multi‐View Feature Selection

Advanced Intelligent Discovery, EarlyView.
This work introduces a novel framework for identifying non‐small cell lung cancer biomarkers from hundreds of volatile organic compounds in breath, analyzed via gas chromatography‐mass spectrometry. This method integrates generative data augmentation and multi‐view feature selection, providing a stable and accurate solution for biomarker discovery in ...
Guancheng Ren, Bohao Liu, Zhiyuan Qu, Xue Jiang, Heng Zhao, Danyao Qu, Ruizhi Ning, Chen Su, Taoping Liu, Guangjian Zhang, Weiwei Wu   +10 more
wiley   +1 more source

Response to ‘Further Detail Concerning the Deep Learning Model for Mortality After Total Gastrectomy’

Annals of Gastroenterological Surgery, EarlyView.
Ryosuke Fukuyo, Hiroyuki Yamamoto, Masanori Tokunaga, Yusuke Kinugasa, Yoshihiro Kakeji, Ken Shirabe   +5 more
wiley   +1 more source

Accelerating Primary Screening of USP8 Inhibitors from Drug Repurposing Databases with Tree‐Based Machine Learning

Advanced Intelligent Discovery, EarlyView.
This study introduces a tree‐based machine learning approach to accelerate USP8 inhibitor discovery. The best‐performing model identified 100 high‐confidence repurposable compounds, half already approved or in clinical trials, and uncovered novel scaffolds not previously studied. These findings offer a solid foundation for rapid experimental follow‐up,
Yik Kwong Ng, Sharon Ai Er Lam, Wei Chun Lim, Hannah Hui Min Toh, Shi Jun Ang   +4 more
wiley   +1 more source

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The use of the area under the ROC curve in the evaluation of machine learning algorithms

Pattern Recognition, 1997
A. Bradley
semanticscholar   +3 more sources

The area under the ROC curve as a measure of clustering quality

Data mining and knowledge discovery, 2020
The area under the receiver operating characteristics (ROC) Curve, referred to as AUC, is a well-known performance measure in the supervised learning domain.
Pablo A. Jaskowiak, Ivan G. Costa, R. Campello   +2 more
semanticscholar   +1 more source

The ROC Curve Redefined - Optimizing Sensitivity (and Specificity) to the Lived Reality of Cancer.

New England Journal of Medicine, 2019
The ROC Curve Redefined Though cancer’s setbacks didn’t necessarily threaten my life, they certainly threatened my days.
S. Walker
semanticscholar   +1 more source

Receiver Operating Characteristic (ROC) Area Under the Curve (AUC): A Diagnostic Measure for Evaluating the Accuracy of Predictors of Education Outcomes

Journal of Education for Students Placed at Risk (JESPAR), 2019
Early Warning Systems (EWS) and Early Warning Indictors (EWI) have recently emerged as an attractive domain for states and school districts interested in predicting student outcomes using data that schools already collect with the intention to better ...
Alex J. Bowers, Xiaoliang Zhou
semanticscholar   +1 more source

[ROC curve].

Semergen, 2022
J. A. Martínez Pérez, P. S. Pérez Martín   +1 more
semanticscholar   +1 more source