Results 151 to 160 of about 179,516 (314)

Uncovering nativity disparities in cancer patterns: Multiple imputation strategy to handle missing nativity data in the Surveillance, Epidemiology, and End Results data file [PDF]

, 2014
Jane R. Montealegre, Renke Zhou, E. Susan Amirian, Michael E. Scheurer   +3 more
openalex   +1 more source

Endothelial GPR68 Is Essential for Arteriogenesis and Represents a Therapeutic Target in a Model of Peripheral Artery Disease

Advanced Science, EarlyView.
Endothelial GPR68 is identified as a critical regulator of collateral artery growth in peripheral artery disease. Genetic and pharmacological evidence demonstrates that GPR68 integrates hemodynamic cues to drive monocyte recruitment and inflammatory remodeling, thereby promoting collateral arteriogenesis and tissue perfusion after ischemia ...
Yiyan Song, Senyi Peng, Zhilei Huang, Miao Yu, Mingyue Wu, Yuexin Zhu, Zeyi Zhou, Yongshan Liu, Yalin Zhang, Yu Huang, Zhongxing Wang, Fu‐Li Xiang, Jie Xu   +12 more
wiley   +1 more source

SGA-DT: An adaptive fusion framework for missing data imputation and interpretable healthcare classification. [PDF]

PLoS One
Jena M, Dehuri S, Cho SB.
europepmc   +1 more source

Control-based imputation for sensitivity analyses in informative censoring for recurrent event data

, 2020
G. Golm, D. Zeng, J.F. Heyse, Fubing Gao, G. Diao, Botao Lin, J.G. Ibrahim, Linqian Xu, Guoxing Liu   +8 more
openalex   +1 more source

Mixture-based Multiple Imputation Model for Clinical Data with a Temporal Dimension [PDF]

, 2019
Ye Xue, Diego Klabjan, Yuan Luo
openalex   +1 more source

Model based Data Imputation

International Journal of Computer Applications, 2022
P. Krishna Subba Rao, Vittanala Sai Bhushan   +1 more
openaire   +1 more source

Integrating Spatial Proteogenomics in Cancer Research

Advanced Science, EarlyView.
Xx xx. ABSTRACT Background: Spatial proteogenomics marks a paradigm shift in oncology by integrating molecular analysis with spatial information from both spatial proteomics and other data modalities (e.g., spatial transcriptomics), thereby unveiling tumor heterogeneity and dynamic changes in the microenvironment.
Yida Wang, Yang Wu, Feng Zhang, Parthiban Periasamy, Haiyue You, Denise Goh, Rachel Elizabeth Ann Fincham, Xin Ning, Danping Wu, Lu Liu, Ying Jiang, Zhiwen Qian, Joe Yeong, Yan Zhang   +13 more
wiley   +1 more source

Proteomics Data Imputation With a Deep Model That Learns From Many Datasets. [PDF]

Mol Cell Proteomics
Harris L, Noble WS.
europepmc   +1 more source

A Guide for Spatial Omics Technologies: Innovation, Evaluation, and Application

Advanced Science, EarlyView.
This review presents a strategy‐centric framework for spatial omics technologies, organizing methods by how spatial information is experimentally encoded. It compares key performance trade‐offs across sequencing‐ and imaging‐based approaches, examines computational and practical limitations, and highlights biomedical applications. The analysis provides
Xiaofeng Wu, Weize Xu, Da Lin, Leqiang Sun, Jinxia Dai, Gang Cao   +5 more
wiley   +1 more source

Prediction of postoperative infections by strategic data imputation and explainable machine learning. [PDF]

J Am Med Inform Assoc
Guillen-Ramirez H, Sanchez-Taltavull D, Perrodin S, Peisl S, Triep K, Gaudet-Blavignac C, Endrich O, Beldi G.   +7 more
europepmc   +1 more source