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Citations per year

Number of citations per year for the bioinformatics software tool GEMS
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Tool usage distribution map

This map represents all the scientific publications referring to GEMS per scientific context
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GEMS specifications

Information


Unique identifier OMICS_24473
Name GEMS
Alternative name Gene Expression Model Selector
Software type Application/Script
Interface Graphical user interface
Restrictions to use Academic or non-commercial use
Operating system Unix/Linux, Mac OS, Windows
Programming languages C++, MATLAB
Computer skills Medium
Version 2.0.2
Stability Stable
Registration required Yes
Maintained No

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Publications for Gene Expression Model Selector

GEMS citations

 (7)
library_books

A Stack based Ensemble Framework for Detecting Cancer MicroRNA Biomarkers

2017
PMCID: 5828659
PMID: 29246520
DOI: 10.1016/j.gpb.2016.10.006

[…] set and test set contained 80 and 187 samples of both benign and malign classes, respectively, with each sample having 44 features. The brain tumor dataset (POM dataset) was obtained from http://www.gems-system.org/, which contains 90 samples with each sample having 5921 features. The training set and test set of POM dataset contained 42 and 48 samples, respectively. […]

library_books

A robust data scaling algorithm to improve classification accuracies in biomedical data

2016
BMC Bioinformatics
PMCID: 5016890
PMID: 27612635
DOI: 10.1186/s12859-016-1236-x

[…] Breast cancer, Colon cancer, Lung cancer, Prostate cancer, and Myeloma were made available by Stantnikov et al. [], and we downloaded the datasets from the supplementary material website (http://www.gems-system.org/). The datasets DLBCL and Leukemia were downloaded from the Kent Ridge Bio-medical Dataset Repository (http://datam.i2r.a-star.edu.sg/datasets/krbd); we removed the variables with miss […]

library_books

Hybrid Binary Imperialist Competition Algorithm and Tabu Search Approach for Feature Selection Using Gene Expression Data

2016
Biomed Res Int
PMCID: 4989135
PMID: 27579323
DOI: 10.1155/2016/9721713

[…] n effective algorithm HICATS to perform feature selection for improving the classification accuracy. The datasets consist of 10 pieces of gene expression data, which can be downloaded from http://www.gems-system.org/. The description of datasets is listed in , which contains the dataset name and detailed expression. gives the related samples, genes, and classes. These datasets contained binary-cl […]

library_books

An experimental study of the intrinsic stability of random forest variable importance measures

2016
BMC Bioinformatics
PMCID: 4739337
PMID: 26842629
DOI: 10.1186/s12859-016-0900-5

[…] asets comes from the application of biology, and 11 from gene expression datasets except Arcene and madelon, are obtained from a repository of the most widely studied gene expression sets (http://www.gems-system.org/) []. The dataset Arcene, madelon and the rest are obtained from UCI Machine Learning Repository (http://archive.ics.uci.edu/ml/). Four dataset indicators are used to describe the char […]

library_books

A p53 Drug Response Signature Identifies Prognostic Genes in High Risk Neuroblastoma

2013
PLoS One
PMCID: 3865347
PMID: 24348903
DOI: 10.1371/journal.pone.0079843

[…] rs such as age at diagnosis (≥12 months vs. <12 months), International Neuroblastoma Staging System (INSS) stage (stage 4 vs. other stages), and MYCN status (amplified vs. non-amplified) were tested. GEMS algorithm was used to construct support vector machine (SVM) predictors using 20-fold cross-validation for each clinical factor: death event (DE), relapse event (RE), and INSS stage. The efficie […]

library_books

Outcome prediction based on microarray analysis: a critical perspective on methods

2009
BMC Bioinformatics
PMCID: 2667512
PMID: 19200394
DOI: 10.1186/1471-2105-10-53

[…] re with high accuracy and good generalization on the independent test-set.• The maximum performance on a 10-fold CV scenario is used to access optimal algorithmic performance under CV, similar to the GEMS approach [,]. The initial set of training samples is randomly partitioned into ten groups. Each group, denoted by fold for the rest of this paper, is iteratively used for testing, whereas the rem […]


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GEMS institution(s)
Discovery Systems Laboratory, Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA
GEMS funding source(s)
Supported by NIH grants RO1 LM007948-01 and P20 LM 007613-01.

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