An R package implementation of multifactor dimensionality reduction

Background A breadth of high-dimensional data is now available with unprecedented numbers of genetic markers and data-mining approaches to variable selection are increasingly being utilized to uncover associations, including potential gene-gene and gene-environment interactions. One of the most comm...
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Autor*in:	Winham, Stacey J [verfasserIn] Motsinger-Reif, Alison A

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2011

Schlagwörter:	Prediction Accuracy Multifactor Dimensionality Reduction Alternative Implementation Balance Accuracy Multifactor Dimensionality Reduction Method

Anmerkung:	© Winham and Motsinger-Reif; licensee BioMed Central Ltd. 2011

Übergeordnetes Werk:	Enthalten in: BioData Mining - London : BioMed Central, 2008, 4(2011), 1 vom: 16. Aug.
Übergeordnetes Werk:	volume:4 ; year:2011 ; number:1 ; day:16 ; month:08

Links:	Volltext

DOI / URN:	10.1186/1756-0381-4-24

Katalog-ID:	SPR029585880

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520			\|a Background A breadth of high-dimensional data is now available with unprecedented numbers of genetic markers and data-mining approaches to variable selection are increasingly being utilized to uncover associations, including potential gene-gene and gene-environment interactions. One of the most commonly used data-mining methods for case-control data is Multifactor Dimensionality Reduction (MDR), which has displayed success in both simulations and real data applications. Additional software applications in alternative programming languages can improve the availability and usefulness of the method for a broader range of users. Results We introduce a package for the R statistical language to implement the Multifactor Dimensionality Reduction (MDR) method for nonparametric variable selection of interactions. This package is designed to provide an alternative implementation for R users, with great flexibility and utility for both data analysis and research. The 'MDR' package is freely available online at http://www.r-project.org/. We also provide data examples to illustrate the use and functionality of the package. Conclusions MDR is a frequently-used data-mining method to identify potential gene-gene interactions, and alternative implementations will further increase this usage. We introduce a flexible software package for R users.
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allfields	10.1186/1756-0381-4-24 doi (DE-627)SPR029585880 (SPR)1756-0381-4-24-e DE-627 ger DE-627 rakwb eng Winham, Stacey J verfasserin aut An R package implementation of multifactor dimensionality reduction 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Winham and Motsinger-Reif; licensee BioMed Central Ltd. 2011 Background A breadth of high-dimensional data is now available with unprecedented numbers of genetic markers and data-mining approaches to variable selection are increasingly being utilized to uncover associations, including potential gene-gene and gene-environment interactions. One of the most commonly used data-mining methods for case-control data is Multifactor Dimensionality Reduction (MDR), which has displayed success in both simulations and real data applications. Additional software applications in alternative programming languages can improve the availability and usefulness of the method for a broader range of users. Results We introduce a package for the R statistical language to implement the Multifactor Dimensionality Reduction (MDR) method for nonparametric variable selection of interactions. This package is designed to provide an alternative implementation for R users, with great flexibility and utility for both data analysis and research. The 'MDR' package is freely available online at http://www.r-project.org/. We also provide data examples to illustrate the use and functionality of the package. Conclusions MDR is a frequently-used data-mining method to identify potential gene-gene interactions, and alternative implementations will further increase this usage. We introduce a flexible software package for R users. Prediction Accuracy (dpeaa)DE-He213 Multifactor Dimensionality Reduction (dpeaa)DE-He213 Alternative Implementation (dpeaa)DE-He213 Balance Accuracy (dpeaa)DE-He213 Multifactor Dimensionality Reduction Method (dpeaa)DE-He213 Motsinger-Reif, Alison A aut Enthalten in BioData Mining London : BioMed Central, 2008 4(2011), 1 vom: 16. Aug. (DE-627)572421893 (DE-600)2438773-3 1756-0381 nnns volume:4 year:2011 number:1 day:16 month:08 https://dx.doi.org/10.1186/1756-0381-4-24 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 4 2011 1 16 08
spelling	10.1186/1756-0381-4-24 doi (DE-627)SPR029585880 (SPR)1756-0381-4-24-e DE-627 ger DE-627 rakwb eng Winham, Stacey J verfasserin aut An R package implementation of multifactor dimensionality reduction 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Winham and Motsinger-Reif; licensee BioMed Central Ltd. 2011 Background A breadth of high-dimensional data is now available with unprecedented numbers of genetic markers and data-mining approaches to variable selection are increasingly being utilized to uncover associations, including potential gene-gene and gene-environment interactions. One of the most commonly used data-mining methods for case-control data is Multifactor Dimensionality Reduction (MDR), which has displayed success in both simulations and real data applications. Additional software applications in alternative programming languages can improve the availability and usefulness of the method for a broader range of users. Results We introduce a package for the R statistical language to implement the Multifactor Dimensionality Reduction (MDR) method for nonparametric variable selection of interactions. This package is designed to provide an alternative implementation for R users, with great flexibility and utility for both data analysis and research. The 'MDR' package is freely available online at http://www.r-project.org/. We also provide data examples to illustrate the use and functionality of the package. Conclusions MDR is a frequently-used data-mining method to identify potential gene-gene interactions, and alternative implementations will further increase this usage. We introduce a flexible software package for R users. Prediction Accuracy (dpeaa)DE-He213 Multifactor Dimensionality Reduction (dpeaa)DE-He213 Alternative Implementation (dpeaa)DE-He213 Balance Accuracy (dpeaa)DE-He213 Multifactor Dimensionality Reduction Method (dpeaa)DE-He213 Motsinger-Reif, Alison A aut Enthalten in BioData Mining London : BioMed Central, 2008 4(2011), 1 vom: 16. Aug. (DE-627)572421893 (DE-600)2438773-3 1756-0381 nnns volume:4 year:2011 number:1 day:16 month:08 https://dx.doi.org/10.1186/1756-0381-4-24 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 4 2011 1 16 08
allfields_unstemmed	10.1186/1756-0381-4-24 doi (DE-627)SPR029585880 (SPR)1756-0381-4-24-e DE-627 ger DE-627 rakwb eng Winham, Stacey J verfasserin aut An R package implementation of multifactor dimensionality reduction 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Winham and Motsinger-Reif; licensee BioMed Central Ltd. 2011 Background A breadth of high-dimensional data is now available with unprecedented numbers of genetic markers and data-mining approaches to variable selection are increasingly being utilized to uncover associations, including potential gene-gene and gene-environment interactions. One of the most commonly used data-mining methods for case-control data is Multifactor Dimensionality Reduction (MDR), which has displayed success in both simulations and real data applications. Additional software applications in alternative programming languages can improve the availability and usefulness of the method for a broader range of users. Results We introduce a package for the R statistical language to implement the Multifactor Dimensionality Reduction (MDR) method for nonparametric variable selection of interactions. This package is designed to provide an alternative implementation for R users, with great flexibility and utility for both data analysis and research. The 'MDR' package is freely available online at http://www.r-project.org/. We also provide data examples to illustrate the use and functionality of the package. Conclusions MDR is a frequently-used data-mining method to identify potential gene-gene interactions, and alternative implementations will further increase this usage. We introduce a flexible software package for R users. Prediction Accuracy (dpeaa)DE-He213 Multifactor Dimensionality Reduction (dpeaa)DE-He213 Alternative Implementation (dpeaa)DE-He213 Balance Accuracy (dpeaa)DE-He213 Multifactor Dimensionality Reduction Method (dpeaa)DE-He213 Motsinger-Reif, Alison A aut Enthalten in BioData Mining London : BioMed Central, 2008 4(2011), 1 vom: 16. Aug. (DE-627)572421893 (DE-600)2438773-3 1756-0381 nnns volume:4 year:2011 number:1 day:16 month:08 https://dx.doi.org/10.1186/1756-0381-4-24 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 4 2011 1 16 08
allfieldsGer	10.1186/1756-0381-4-24 doi (DE-627)SPR029585880 (SPR)1756-0381-4-24-e DE-627 ger DE-627 rakwb eng Winham, Stacey J verfasserin aut An R package implementation of multifactor dimensionality reduction 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Winham and Motsinger-Reif; licensee BioMed Central Ltd. 2011 Background A breadth of high-dimensional data is now available with unprecedented numbers of genetic markers and data-mining approaches to variable selection are increasingly being utilized to uncover associations, including potential gene-gene and gene-environment interactions. One of the most commonly used data-mining methods for case-control data is Multifactor Dimensionality Reduction (MDR), which has displayed success in both simulations and real data applications. Additional software applications in alternative programming languages can improve the availability and usefulness of the method for a broader range of users. Results We introduce a package for the R statistical language to implement the Multifactor Dimensionality Reduction (MDR) method for nonparametric variable selection of interactions. This package is designed to provide an alternative implementation for R users, with great flexibility and utility for both data analysis and research. The 'MDR' package is freely available online at http://www.r-project.org/. We also provide data examples to illustrate the use and functionality of the package. Conclusions MDR is a frequently-used data-mining method to identify potential gene-gene interactions, and alternative implementations will further increase this usage. We introduce a flexible software package for R users. Prediction Accuracy (dpeaa)DE-He213 Multifactor Dimensionality Reduction (dpeaa)DE-He213 Alternative Implementation (dpeaa)DE-He213 Balance Accuracy (dpeaa)DE-He213 Multifactor Dimensionality Reduction Method (dpeaa)DE-He213 Motsinger-Reif, Alison A aut Enthalten in BioData Mining London : BioMed Central, 2008 4(2011), 1 vom: 16. Aug. (DE-627)572421893 (DE-600)2438773-3 1756-0381 nnns volume:4 year:2011 number:1 day:16 month:08 https://dx.doi.org/10.1186/1756-0381-4-24 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 4 2011 1 16 08
allfieldsSound	10.1186/1756-0381-4-24 doi (DE-627)SPR029585880 (SPR)1756-0381-4-24-e DE-627 ger DE-627 rakwb eng Winham, Stacey J verfasserin aut An R package implementation of multifactor dimensionality reduction 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Winham and Motsinger-Reif; licensee BioMed Central Ltd. 2011 Background A breadth of high-dimensional data is now available with unprecedented numbers of genetic markers and data-mining approaches to variable selection are increasingly being utilized to uncover associations, including potential gene-gene and gene-environment interactions. One of the most commonly used data-mining methods for case-control data is Multifactor Dimensionality Reduction (MDR), which has displayed success in both simulations and real data applications. Additional software applications in alternative programming languages can improve the availability and usefulness of the method for a broader range of users. Results We introduce a package for the R statistical language to implement the Multifactor Dimensionality Reduction (MDR) method for nonparametric variable selection of interactions. This package is designed to provide an alternative implementation for R users, with great flexibility and utility for both data analysis and research. The 'MDR' package is freely available online at http://www.r-project.org/. We also provide data examples to illustrate the use and functionality of the package. Conclusions MDR is a frequently-used data-mining method to identify potential gene-gene interactions, and alternative implementations will further increase this usage. We introduce a flexible software package for R users. Prediction Accuracy (dpeaa)DE-He213 Multifactor Dimensionality Reduction (dpeaa)DE-He213 Alternative Implementation (dpeaa)DE-He213 Balance Accuracy (dpeaa)DE-He213 Multifactor Dimensionality Reduction Method (dpeaa)DE-He213 Motsinger-Reif, Alison A aut Enthalten in BioData Mining London : BioMed Central, 2008 4(2011), 1 vom: 16. Aug. (DE-627)572421893 (DE-600)2438773-3 1756-0381 nnns volume:4 year:2011 number:1 day:16 month:08 https://dx.doi.org/10.1186/1756-0381-4-24 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 4 2011 1 16 08
language	English
source	Enthalten in BioData Mining 4(2011), 1 vom: 16. Aug. volume:4 year:2011 number:1 day:16 month:08
sourceStr	Enthalten in BioData Mining 4(2011), 1 vom: 16. Aug. volume:4 year:2011 number:1 day:16 month:08
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Prediction Accuracy Multifactor Dimensionality Reduction Alternative Implementation Balance Accuracy Multifactor Dimensionality Reduction Method
isfreeaccess_bool	true
container_title	BioData Mining
authorswithroles_txt_mv	Winham, Stacey J @@aut@@ Motsinger-Reif, Alison A @@aut@@
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One of the most commonly used data-mining methods for case-control data is Multifactor Dimensionality Reduction (MDR), which has displayed success in both simulations and real data applications. Additional software applications in alternative programming languages can improve the availability and usefulness of the method for a broader range of users. Results We introduce a package for the R statistical language to implement the Multifactor Dimensionality Reduction (MDR) method for nonparametric variable selection of interactions. This package is designed to provide an alternative implementation for R users, with great flexibility and utility for both data analysis and research. The 'MDR' package is freely available online at http://www.r-project.org/. We also provide data examples to illustrate the use and functionality of the package. 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author	Winham, Stacey J
spellingShingle	Winham, Stacey J misc Prediction Accuracy misc Multifactor Dimensionality Reduction misc Alternative Implementation misc Balance Accuracy misc Multifactor Dimensionality Reduction Method An R package implementation of multifactor dimensionality reduction
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topic_title	An R package implementation of multifactor dimensionality reduction Prediction Accuracy (dpeaa)DE-He213 Multifactor Dimensionality Reduction (dpeaa)DE-He213 Alternative Implementation (dpeaa)DE-He213 Balance Accuracy (dpeaa)DE-He213 Multifactor Dimensionality Reduction Method (dpeaa)DE-He213
topic	misc Prediction Accuracy misc Multifactor Dimensionality Reduction misc Alternative Implementation misc Balance Accuracy misc Multifactor Dimensionality Reduction Method
topic_unstemmed	misc Prediction Accuracy misc Multifactor Dimensionality Reduction misc Alternative Implementation misc Balance Accuracy misc Multifactor Dimensionality Reduction Method
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title	An R package implementation of multifactor dimensionality reduction
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title_full	An R package implementation of multifactor dimensionality reduction
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title_sort	r package implementation of multifactor dimensionality reduction
title_auth	An R package implementation of multifactor dimensionality reduction
abstract	Background A breadth of high-dimensional data is now available with unprecedented numbers of genetic markers and data-mining approaches to variable selection are increasingly being utilized to uncover associations, including potential gene-gene and gene-environment interactions. One of the most commonly used data-mining methods for case-control data is Multifactor Dimensionality Reduction (MDR), which has displayed success in both simulations and real data applications. Additional software applications in alternative programming languages can improve the availability and usefulness of the method for a broader range of users. Results We introduce a package for the R statistical language to implement the Multifactor Dimensionality Reduction (MDR) method for nonparametric variable selection of interactions. This package is designed to provide an alternative implementation for R users, with great flexibility and utility for both data analysis and research. The 'MDR' package is freely available online at http://www.r-project.org/. We also provide data examples to illustrate the use and functionality of the package. Conclusions MDR is a frequently-used data-mining method to identify potential gene-gene interactions, and alternative implementations will further increase this usage. We introduce a flexible software package for R users. © Winham and Motsinger-Reif; licensee BioMed Central Ltd. 2011
abstractGer	Background A breadth of high-dimensional data is now available with unprecedented numbers of genetic markers and data-mining approaches to variable selection are increasingly being utilized to uncover associations, including potential gene-gene and gene-environment interactions. One of the most commonly used data-mining methods for case-control data is Multifactor Dimensionality Reduction (MDR), which has displayed success in both simulations and real data applications. Additional software applications in alternative programming languages can improve the availability and usefulness of the method for a broader range of users. Results We introduce a package for the R statistical language to implement the Multifactor Dimensionality Reduction (MDR) method for nonparametric variable selection of interactions. This package is designed to provide an alternative implementation for R users, with great flexibility and utility for both data analysis and research. The 'MDR' package is freely available online at http://www.r-project.org/. We also provide data examples to illustrate the use and functionality of the package. Conclusions MDR is a frequently-used data-mining method to identify potential gene-gene interactions, and alternative implementations will further increase this usage. We introduce a flexible software package for R users. © Winham and Motsinger-Reif; licensee BioMed Central Ltd. 2011
abstract_unstemmed	Background A breadth of high-dimensional data is now available with unprecedented numbers of genetic markers and data-mining approaches to variable selection are increasingly being utilized to uncover associations, including potential gene-gene and gene-environment interactions. One of the most commonly used data-mining methods for case-control data is Multifactor Dimensionality Reduction (MDR), which has displayed success in both simulations and real data applications. Additional software applications in alternative programming languages can improve the availability and usefulness of the method for a broader range of users. Results We introduce a package for the R statistical language to implement the Multifactor Dimensionality Reduction (MDR) method for nonparametric variable selection of interactions. This package is designed to provide an alternative implementation for R users, with great flexibility and utility for both data analysis and research. The 'MDR' package is freely available online at http://www.r-project.org/. We also provide data examples to illustrate the use and functionality of the package. Conclusions MDR is a frequently-used data-mining method to identify potential gene-gene interactions, and alternative implementations will further increase this usage. We introduce a flexible software package for R users. © Winham and Motsinger-Reif; licensee BioMed Central Ltd. 2011
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title_short	An R package implementation of multifactor dimensionality reduction
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An R package implementation of multifactor dimensionality reduction

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