Overcoming missing data in spatial metabolomics with machine learning imputation to accelerate downstream discovery. [PDF]
Feng T +8 more
europepmc +1 more source
Tumour heterogeneity and clonal evolution of metastatic salivary gland cancer were evaluated in two patients with adenoid carcinoma and one patient with myoepithelial carcinoma. Radiology‐guided autopsy enabled multi‐region sampling (total samples n = 149), followed by whole‐genome sequencing and phylogenetic reconstruction (17 tumour samples, 4–7 per ...
Gerben Lassche +10 more
wiley +1 more source
Crossing Borders, Missing Data - Cancer Inequities in Displaced and Migrant Populations: A Scoping Review. [PDF]
Godinich BM +4 more
europepmc +1 more source
A Missing Data Imputation Method for Gas Time Series Based on Spatio-Temporal Graph Attention Network-Echo State Network. [PDF]
Yang J, Qin K, Ye J, Zhao Y, Shu L.
europepmc +1 more source
Machine learning for missing data imputation in Alzheimer's research: predicting medial temporal lobe dynamic flexibility. [PDF]
Moallemian S +7 more
europepmc +1 more source
Robust transfer learning for individualized treatment rules in the presence of missing data. [PDF]
Sui Z, Ding Y, Tang L.
europepmc +1 more source
How to prevent missing data in cost-effectiveness analyses alongside cardiovascular disease trials? A review on methods of administration of patient-reported outcomes. [PDF]
Generaal MI +4 more
europepmc +1 more source
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If left unattended, missingness can undermine the conclusions nurse researchers draw in their studies. Unfortunately, a cursory review of quantitative and mixed methods works recently published in the Journal of Nursing Education suggests missing data is left relatively unattended within our community.
Abdulaziz, Abaoud, John M, Taylor
openaire +2 more sources
The Case of the Missing DataThis animated video explores how investigators approach missing data in clinical trials.
Katherine S, Takvorian +2 more
openaire +2 more sources

