Results 121 to 130 of about 310,651 (297)

Receiver operating characteristic (ROC) curves for key parameters.

, 2015
Receiver operating characteristic (ROC) curves for key parameters.
Xinjian Yang (660091), Linkai Jing (765263), Yang Wang (5921), Shengzhang Wang (285770), Haiyun Li (444679), Jixing Fan (765264), Ying Zhang (40767)   +6 more
core   +1 more source

Leveraging Artificial Intelligence and Large Language Models for Cancer Immunotherapy

Advanced Science, EarlyView.
Cancer immunotherapy faces challenges in predicting treatment responses and understanding resistance mechanisms. Artificial intelligence (AI) and machine learning (ML) offer powerful solutions for cancer immunotherapy in patient stratification, biomarker discovery, treatment strategy optimization, and foundation model development.
Xinchao Wu, Mengtao Sun, Lusheng Li, Jieqiong Wang, Shibiao Wan   +4 more
wiley   +1 more source

Beyond the ROC Curve: Activity Monitoring to Evaluate Deep Learning Models in Clinical Settings

Applied Medical Informatics
We evaluated ‘VITALCARE-SEPS’, a deep learning model for sepsis prediction, using the activity monitoring operator characteristics curve with two different scoring algorithms.
Hyunwoo CHOO, Kyung Hyun LEE, Sungsoo HONG, Sungjun HONG, Ki-Byung LEE, Chang Youl LEE   +5 more
doaj  

Coordinates of the receiver operating characteristic (ROC) curve.

, 2019
Coordinates of the receiver operating characteristic (ROC) curve.
Ng Chong Guan (6400754), Zuraida Ahmad Sabki (6400757), Nor Aziyan Yahaya (6400760), Nurul Izzah Shari (6400751), Nor Zuraida Zainal (3838729)   +4 more
core   +1 more source

Mass‐Produced and High‐Performance Nanowell Biosensor Fabricated via Semiconductor Manufacturing for Rapid and Accurate COVID‐19 Diagnosis in the Clinical Field

Advanced Science, EarlyView.
A semiconductor‐fabricated nanowell biosensor enables rapid, scalable, and highly reproducible detection of SARS‐CoV‐2 antigens from nasal swabs within ∼10 minutes. Clinical validation in 249 retrospective and 243 prospective patient samples demonstrates high sensitivity and specificity, minimal cross‐reactivity, and robust batch‐to‐batch ...
Yoo Min Park, Zahra Rezaei, Nam Ho Bae, Donggee Rho, Da‐Seul Kim, EunYoung Go, YoungTae Seo, Seok Jae Lee, Won Chan Seo, Luke P. Lee, HeaYeon Lee, Su Ryon Shin   +11 more
wiley   +1 more source

Receiver operating characteristic (ROC) analysis for selected biomarkers.

, 2013
Receiver operating characteristic (ROC) analysis for selected biomarkers.
Marie Tomandlova (293659), Jiri Ondrasek (293648), Josef Tomandl (293647), Jolana Lipkova (293658), Zdenka Cermakova (293655), Martin Poloczek (49862), Monika Pavkova-Goldbergova (293649), Martin Tretina (293650), Jiri Jarkovsky (293651), Petr Pokorny (293653), Jindrich Spinar (293654), Simona Littnerova (293652), Petr Kala (49855), Roman Miklik (3337497), Ondrej Pes (293657), Jiri Parenica (293645), Petr Malik (293656), Petr Nemec (293646)   +17 more
core   +1 more source

MEOX1 Coordinates Autocrine‐Paracrine Programs via SPHK1/S1P to Promote Lymph Node Metastasis in Ovarian Cancer

Advanced Science, EarlyView.
In ovarian cancer, MEOX1 activates the SPHK1/S1P pathway to promote both tumor progression and tumor–stroma crosstalk. MEOX1‐dependent signaling drives CAF activation, enhances VEGF‐C expression, and stimulates lymphangiogenesis, ultimately facilitating lymph node metastasis.
Jiajia Li, Xiuling Zhi, Qianhan Lin, Yating Sun, Yihua Sun, Zulimire Abudousalamu, Mengyang Xue, Chang Zheng, Xiaotian Li, Liangqing Yao, Mo Chen   +10 more
wiley   +1 more source

Validation procedures in radiological diagnostic models. Neural network and logistic regression [PDF]

The objective of this paper is to compare the performance of two predictive radiological models, logistic regression (LR) and neural network (NN), with five different resampling methods.
Pedro Delicado, Luis Martí, Estanislao Arana   +2 more
core  

INB3P: A Multi‐Modal and Interpretable Co‐Attention Framework Integrating Property‐Aware Explanations and Memory‐Bank Contrastive Fusion for Blood–Brain Barrier Penetrating Peptide Discovery

Advanced Science, EarlyView.
INB3P is a multimodal framework for blood–brain barrier‐penetrating peptide prediction under extreme data scarcity and class imbalance. By combining physicochemical‐guided augmentation, sequence–structure co‐attention, and imbalance‐aware optimization, it improves predictive performance and interpretability.
Jingwei Lv, Qianyang Wu, Jian Liu, Binlu Yang, Yuanhao Li, Junlin Xu, Yajie Meng, Leyi Wei, Zilong Zhang, Quan Zou, Xiulai Li, Feifei Cui   +11 more
wiley   +1 more source

Parameters behind "nonparametric" statistics: Kendall's tau,Somers' D and median differences

So-called "nonparametric" statistical methods are often in fact based on population parameters, which can be estimated (with confidence limits) using the corresponding sample statistics.
Roger Newson
core