Results 171 to 180 of about 371,316 (301)

The Application of Bayesian Methods in Cancer Prognosis and Prediction. [PDF]

Cancer Genomics Proteomics, 2022
Chu J, Sun NA, Hu W, Chen X, Yi N, Shen Y.   +5 more
europepmc   +1 more source

Integrated Single‐Cell and Spatial Analysis Reveals a Metabolic‐Immune Axis Driving Aortic Dissection

Advanced Science, EarlyView.
Single‐cell and spatial profiling of 110 human thoracic aortic samples reveals a stromal–immune circuit driving aortic dissection. An elastin‐rich fibroblast subset is depleted with age and markedly reduced in disease, weakening aortic wall integrity.
Jing Tao, Huanjie Yang, Jiahui Yong, Xueting Chen, Qiang Zhao, Xueli Wu, Lei Yan, Li Pang, Fan Luo, Mengjun Yu, Shanshan Pan, Deyang Li, Rouxi Chen, Juan Wang, Zhensheng Dong, Fan Yang, Yue Wang, Yang Chen, Hongjian Zheng, Zhimin Yang, Zijie Wang, Karsten Kristiansen, Hui Peng, Xiaodong Fang, Juan Shen, Yining Yang   +25 more
wiley   +1 more source

Bayesian analysis of data and model uncertainty in 3D seismic travel-time tomography

Open Geosciences, 2009
Arvanitis Michael, Vlamos Panagiotis
doaj   +1 more source

A comparison of frequentist and Bayesian methods for meta-analysis of diagnostic test accuracy studies. [PDF]

Epidemiol Infect
Rousou X, Furuya-Kanamori L, Kostoulas P, Doi SAR, Meletis E, Solomakos N.   +5 more
europepmc   +1 more source

<i>In silico</i>development and validation of Bayesian methods for optimizing deep brain stimulation to enhance cognitive control. [PDF]

J Neural Eng, 2023
Nagrale SS, Yousefi A, Netoff TI, Widge AS.   +3 more
europepmc   +1 more source

STAID: A Self‐Refining Deep Learning Framework for Spatial Cell‐Type Deconvolution with Biologically Informed Modeling

Advanced Science, EarlyView.
STAID is a unified deep learning framework that couples iterative pseudo‐spot refinement with neural network training through a feedback loop and exploits gene co‐expression information to model higher‐order interactions, achieving accurate and robust cell‐type deconvolution in spatial transcriptomics.
Jixin Liu, Shuli Sun, Zhengliang Lv, Xinyu Liu, Yihua Wang, Bingqiang Liu   +5 more
wiley   +1 more source

The use of Bayesian methods for the analysis of Studies Within A Trial: a proof-of-concept case study. [PDF]

Trials
Cro S, He Z, Babalis D, Billot L.
europepmc   +1 more source

Comparison of Bayesian methods for incorporating adult clinical trial data to improve certainty of treatment effect estimates in children. [PDF]

PLoS One, 2023
Walker R, Phillips B, Dias S.
europepmc   +1 more source

A Data‐Driven Inverse Design Methodology for Magnetic Soft Millirobots Navigating in Confined Spaces

Advanced Science, EarlyView.
A data‐efficient inverse design framework automates the optimization of magnetic soft millirobots for confined‐space navigation. Integrating a physics‐based Cosserat rod model with Bayesian optimization efficiently identifies high‐performance geometries.
Ziyu Ren, Hong Wang, Chak Wang Tse, Yi Zheng, Xi Chen, Wenqi Hu   +5 more
wiley   +1 more source

The Contribution of Bayesian Methods in Solving the Paradoxes of Classical Statistical Tests in Biomedical Research. [PDF]

J Clin Med
Meyer N.
europepmc   +1 more source