GEMS: A system for automated cancer diagnosis and biomarker discovery from microarray gene expression data

By Alexander R. Statnikov, Ioannis Tsamardinos, Yerbolat Dosbayev, Constantin F. Aliferis. 2005

199 citations recorded by the source corpus.

Subfields: Biophysics, Molecular Biology

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In Molecular Biology

Citations in the corpus

Federated learning of predictive models from federated Electronic Health Records (2018, background): “…Vector Machines; the applications range from prediction of the onset of diabetes,[1,2] prediction of hospitalizations for cardiac events,[3] prediction of medication adherence in heart failure patients,[4] and cancer diagnosis,[5] to automated recognition of the obstructive sleep apnea syndrome .”
Swarm Intelligence Algorithms for Feature Selection: A Review (2018, methodology): “The majority of datasets is available from four sources, i.e., UCI Machine Learning Repository [69], Gems System [70], LIBSVM Data [71], and Kent Ridge Bio-medical Dataset [72].”
Local Causal and Markov Blanket Induction for Causal Discovery and Feature Selection for Classification Part I: Algorithms and Empirical Evaluation (2010, methodology): “…been applied for the discovery of biomarkers in human cancer data using microarrays and mass spectrometry and is also implemented in the GEMS and FAST-AIMS systems for the automated analysis of microarray and mass spectrometry data respectively (Statnikov et al., 2005b; Fananapazir etal., 2005).”
An experimental study of the intrinsic stability of random forest variable importance measures (2016, unknown): “Among them, 14 of the total 19 datasets comes from the application of biology, and 11 from gene expression datasets except Arcene and made-lon , are obtained from a repository of the most widely studied gene expression sets (http://www.gems-system. org/) [40].”
A comprehensive evaluation of multicategory classification methods for microbiomic data (2013, methodology): “To account for variance in accuracy estimation, we repeated this entire process (nested 10-fold cross-validation) for 10 different splits of the data into 10 cross-validation testing sets and averaged the results [29].”
A comprehensive survey on recent metaheuristics for feature selection (2022, background): “For the treatment of cancer patients, five new datasets are introduced in a study that proposes the Gene Expression Model Selector (GEMS) system [272].”
A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification (2008, background): “Details about the "nested cross-validation" procedure can be found in [19,20].”