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mRMR specifications


Unique identifier OMICS_22995
Name mRMR
Alternative name minimum Redundancy Maximum Relevance


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Publication for minimum Redundancy Maximum Relevance

mRMR citations


Histogram Based Features Selection and Volume of Interest Ranking for Brain PET Image Classification

PMCID: 5881487
PMID: 29637029
DOI: 10.1109/JTEHM.2018.2796600

[…] state-of-art of Feature Selection methods (FS) including Student’s t-Test analysis , Fisher score , , Support vector Machine feature elimination (SVM-RFE) , feature selection with Random Forest , , , minimum Redundancy Maximum Relevance (mRMR) , and ReliefF , , . The Voxel Based Analysis (VBA), considered as a baseline classification approach is also used for comparison purposes. It consists in in […]


Intrusion detection system using Online Sequence Extreme Learning Machine (OS ELM) in advanced metering infrastructure of smart grid

PLoS One
PMCID: 5828363
PMID: 29485990
DOI: 10.1371/journal.pone.0192216
call_split See protocol

[…] he dimension. So we need to use the method of reducing the feature dimension.In order to validate the effectiveness and feasibility of the proposed method, we compare this method with Fisher, Relief, mRMR and InfoGain, which are the methods of feature selection in this paper.In the same system environment, the OS-ELM algorithm with the number of hidden nodes is 200, and the activation function sel […]


50/50 Expressional Odds of Retention Signifies the Distinction between Retained Introns and Constitutively Spliced Introns in Arabidopsis thaliana

Front Plant Sci
PMCID: 5640774
PMID: 29062321
DOI: 10.3389/fpls.2017.01728

[…] PKM occurring between RIs and CSIs (15.2323 vs. 25.2828, averagely). It is interesting to note that the FPKM feature ranks in the top3 when we evaluate our selected features of classification through mRMR method, the “ATTTT” and SFaccvalue features sort in the top1 and top2 respectively (File S3). Meanwhile FPKM feature proved to dramatically contribute in distinguishing RIs from CSIs by our exper […]


Identifying and analyzing different cancer subtypes using RNA seq data of blood platelets

PMCID: 5675649
PMID: 29152097
DOI: 10.18632/oncotarget.20903

[…] ancer subtypes from healthy controls. The gene expression profiles of blood from patients who had one of six cancer subtypes and healthy persons were analyzed by maximum relevance minimum redundancy (mRMR) []. Upon further analysis of the feature lists yielded by the mRMR method, eighteen important genes were extracted that may be essential biomarkers for the classification of cancer subtypes and […]


Prediction and analysis of essential genes using the enrichments of gene ontology and KEGG pathways

PLoS One
PMCID: 5584762
PMID: 28873455
DOI: 10.1371/journal.pone.0184129

[…] n one gene and one GO term or KEGG pathway was encoded into a numeric value using the enrichment theory of GO and KEGG. Then, some popular computational methods, maximum relevance minimum redundancy (mRMR) [], incremental feature selection (IFS), and a support vector machine (SVM) [, ], were employed to analyze involved GO terms and KEGG pathways. As a result, some key GO terms and KEGG pathways w […]


A Cancer Gene Selection Algorithm Based on the K S Test and CFS

Biomed Res Int
PMCID: 5439177
PMID: 28567418
DOI: 10.1155/2017/1645619

[…] plied to select a compact yet effective gene subset from the candidate set. Comprehensive experiments were conducted to compare the K-S test-CFS selection algorithm to the K-S test, CFS, ReliefF, and mRMR feature selection methods using the SVM classifier on five different datasets. The experimental results show that the K-S test-CFS gene selection is an effective method compared to the K-S test, […]

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mRMR institution(s)
Computational Research Division, Lawrence Berkeley National Laboratory, University of California at Berkeley, Berkeley, CA, USA;

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