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CEM Corporation classification expectation maximization (cem)
Classification Expectation Maximization (Cem), supplied by CEM Corporation, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/expectation-maximization+algorithm/classification+expectation+maximization+algorithm/pmc03573927-164-2-5
Average 90 stars, based on 1 article reviews
classification expectation maximization (cem) - by Bioz Stars, 2026-10
90/100 stars

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Article Title: Effect of prior antibiotic or chemotherapy treatment on immunotherapy response in non-small cell lung cancer
Article Snippet: Relative risk (RR) regression using a log-link with combinatorial expectation maximization (CEM) algorithm [ ] was used to assess the association between recent ATB use and other risk factors with clinical response.

Article Title: Combining the strengths of inverse-variance weighting and Egger regression in Mendelian randomization using a mixture of regressions model
Article Snippet: In practice, we do not know which IVs are invalid (and all the parameter values), thus we apply the classification expectation-maximization (CEM), a variant of EM algorithm, to classify IVs (and estimate the parameters) [ , ].

Article Title: Reflection on modern methods: risk ratio regression—simple concept yet complex computation
Article Snippet: In such situations, computational methods like combinatorial expectation maximization (CEM), adaptive barrier method, parabolic expectation maximization (PEM)/quasi Newton methods may be used through packages such as logbin in R. Use of these latter computational methods may allow convergence if the starting values are specified.

Article Title: Combining the strengths of inverse-variance weighting and Egger regression in Mendelian randomization using a mixture of regressions model
Article Snippet: The model is fitted using a classification expectation-maximization (CEM) algorithm [ , ], selecting valid and invalid IVs to be used by IVW (fixed effects) and Egger regression respectively.

Article Title: Improving the accuracy and internal consistency of regression-based clustering of high-dimensional datasets.
Article Snippet: Component-wise Sparse Mixture Regression (CSMR) is a recently proposed regression-based clusteringmethod that shows promise in detecting heterogeneous relationships betweenmolecularmarkers and a continuous phenotype of interest.. However, CSMR can yield inconsistent results when applied to high-dimensional molecular data, which we hypothesize is in part due to inherent limitations associated with the feature selection method used in the CSMR algorithm.. To assess this hypothesis, we explored whether substituting different regularized regression methods (i.e. Lasso, Elastic Net, Smoothly Clipped Absolute Deviation (SCAD), Minmax Convex Penalty (MCP), and Adaptive-Lasso) within the CSMR framework can improve the clustering accuracy and internal consistency (IC) of CSMR in high-dimensional settings.

Article Title: Weighted likelihood methods for robust fitting of wrapped models for p-torus data
Article Snippet: They are both based on a set of weighted data-augmented estimating equations that are solved using a classification expectation-maximization (CEM) algorithm, whose M-step is enhanced by the computation of a set of data dependent weights aimed to down-weight outliers.

Article Title: Effect of Prior Antibiotic or Chemotherapy Treatment on Immunotherapy Response in Non-small Cell Lung Cancer
Article Snippet: Relative risk (RR) regression using a log-link with combinatorial expectation maximization (CEM) algorithm (25) was used to assess the association between recent ATB use and other risk factors with clinical response.

Article Title: A Tensor-EM Method for Large-Scale Latent Class Analysis with Binary Responses.
Article Snippet: The convergence criterion for EM(CEM) algorithm is set as when the improvement in likelihood is less than 0.1 (which has a relative tolerance smaller than 10−5 in the considered simulation settings).



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