Results 151 to 160 of about 19,319 (259)
ABSTRACT Background Inflammation and immune dysfunction may worsen the prognosis of patients with ST‐segment elevation myocardial infarction (STEMI). Aims This study aimed to assess whether the high‐sensitivity C‐reactive protein–albumin–lymphocyte index (hsCALLYI) could be used to predict the prognosis of STEMI patients who received emergency ...
Xinuo Ma +8 more
wiley +1 more source
SPARCC: Semi-Parametric Robust Estimation in a Right-Censored Covariate Model. [PDF]
Lee SH +4 more
europepmc +1 more source
Features the major cell type compositions among colon and liver metastasis. (A) The study design for single‐cell data analysis. (B) The annotated major cell types. Each type was labeled with a special color. (C) Dot plot of canonical marker genes for major cell types. (D) Bar plot of the percentage for each cell type in individuals. (E) Bar plot of the
Zhixun Zhao +10 more
wiley +1 more source
Causal K-Means Clustering. [PDF]
Kim K, Kim J, Kennedy EH.
europepmc +1 more source
Abstract This study investigates the effect of moisture on CO2 adsorption in South African coals using both experimental and machine learning approaches. Three coal samples (SL, TN, and EM) with varying ranks (RoVmr: 3.49%, 1.26%, and 0.64%, respectively) were collected from different regions of South Africa.
Kasturie Premlall +3 more
wiley +1 more source
A two-stage GAN-based instrumental variable method for causal analysis of omics data. [PDF]
Zhou Y +7 more
europepmc +1 more source
This study shows that integrating multiple machine learning models with optimization and decision‐making improves chemical process design, and that a consensus‐based strategy across models provides more robust and reliable operating recommendations than any single model, especially under limited or noisy data conditions.
Farough Agin +2 more
wiley +1 more source
Estimation of space-time self-exciting point process models using multi-dimensional Gaussian-type exponent approximation. [PDF]
Nketiah EA, Li C, Yang W, Jing Y, Guo P.
europepmc +1 more source
Partial identification with categorical data and nonignorable missing outcomes
Abstract Nonignorable missing outcomes are common in real‐world datasets and often require strong parametric assumptions to achieve identification. These assumptions can be implausible or untestable, and so we may wish to forgo them in favour of partially identified models that narrow the set of a priori possible values to an identification region.
Daniel Daly‐Grafstein, Paul Gustafson
wiley +1 more source
A general framework for adaptive nonparametric dimensionality reduction. [PDF]
Di Noia A, Ravenda F, Mira A.
europepmc +1 more source

