Power comparisons between AntEpiSeeker(A), DECMDR(D), HS-MMGKG(G), SEE(S), SHEIB-AGM(B), SNPHarvester(H), SNPRuler(R) and Epi-SSA(P) on the DNME 1000 dataset.

<p>Power comparisons between AntEpiSeeker(A), DECMDR(D), HS-MMGKG(G), SEE(S), SHEIB-AGM(B), SNPHarvester(H), SNPRuler(R) and Epi-SSA(P) on the DNME 1000 dataset.</p>

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Bibliographic Details
Main Author:	Liyan Sun (760586) (author)
Other Authors:	Jingwen Bian (19942771) (author), Yi Xin (146413) (author), Linqing Jiang (19942774) (author), Linxuan Zheng (19942777) (author)
Published:	2024
Subjects:	Biochemistry Genetics Molecular Biology Cancer Computational Biology Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified sparrow search algorithm multiple objective functions experimental results demonstrate comparative toxicogenomics database alongside 800 case algorithms &# 8217 800 control samples detecting epistatic interactions ssa draws inspiration ssa algorithm outperforms exploring complex diseases xlink "> genome uncovering numerous genes order epistatic interactions highest average f data set increases seven complex diseases epistasis detection tasks dnme 100 datasets complex diseases xlink "> data increases average f detecting epistasis dnme 100 five datasets dnme 1000 order epistasis epistasis rises dnme3 100 dme 100 wtccc dataset source code six third significant role relatedly reported properties consistent potent tool population based otherwise paralleling might play marginal effects identifying higher higher level gene pairs dme 1000 detect high ctd ). crucial factor comprehensive comparison better utilized aids us 6sqwj /</
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_version_	1852025694805032960
author	Liyan Sun (760586)
author2	Jingwen Bian (19942771) Yi Xin (146413) Linqing Jiang (19942774) Linxuan Zheng (19942777)
author2_role	author author author author
author_facet	Liyan Sun (760586) Jingwen Bian (19942771) Yi Xin (146413) Linqing Jiang (19942774) Linxuan Zheng (19942777)
author_role	author
dc.creator.none.fl_str_mv	Liyan Sun (760586) Jingwen Bian (19942771) Yi Xin (146413) Linqing Jiang (19942774) Linxuan Zheng (19942777)
dc.date.none.fl_str_mv	2024-10-24T17:48:50Z
dc.identifier.none.fl_str_mv	10.1371/journal.pone.0311223.s036
dc.relation.none.fl_str_mv	https://figshare.com/articles/dataset/Power_comparisons_between_AntEpiSeeker_A_DECMDR_D_HS-MMGKG_G_SEE_S_SHEIB-AGM_B_SNPHarvester_H_SNPRuler_R_and_Epi-SSA_P_on_the_DNME_1000_dataset_/27295435
dc.rights.none.fl_str_mv	CC BY 4.0 info:eu-repo/semantics/openAccess
dc.subject.none.fl_str_mv	Biochemistry Genetics Molecular Biology Cancer Computational Biology Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified sparrow search algorithm multiple objective functions experimental results demonstrate comparative toxicogenomics database alongside 800 case algorithms &# 8217 800 control samples detecting epistatic interactions ssa draws inspiration ssa algorithm outperforms exploring complex diseases xlink "> genome uncovering numerous genes order epistatic interactions highest average f data set increases seven complex diseases epistasis detection tasks dnme 100 datasets complex diseases xlink "> data increases average f detecting epistasis dnme 100 five datasets dnme 1000 order epistasis epistasis rises dnme3 100 dme 100 wtccc dataset source code six third significant role relatedly reported properties consistent potent tool population based otherwise paralleling might play marginal effects identifying higher higher level gene pairs dme 1000 detect high ctd ). crucial factor comprehensive comparison better utilized aids us 6sqwj /</
dc.title.none.fl_str_mv	Power comparisons between AntEpiSeeker(A), DECMDR(D), HS-MMGKG(G), SEE(S), SHEIB-AGM(B), SNPHarvester(H), SNPRuler(R) and Epi-SSA(P) on the DNME 1000 dataset.
dc.type.none.fl_str_mv	Dataset info:eu-repo/semantics/publishedVersion dataset
description	<p>Power comparisons between AntEpiSeeker(A), DECMDR(D), HS-MMGKG(G), SEE(S), SHEIB-AGM(B), SNPHarvester(H), SNPRuler(R) and Epi-SSA(P) on the DNME 1000 dataset.</p>
eu_rights_str_mv	openAccess
id	Manara_29a8b3dcd0d047b853d93aa8904bb457
identifier_str_mv	10.1371/journal.pone.0311223.s036
network_acronym_str	Manara
network_name_str	ManaraRepo
oai_identifier_str	oai:figshare.com:article/27295435
publishDate	2024
repository.mail.fl_str_mv
repository.name.fl_str_mv
repository_id_str
rights_invalid_str_mv	CC BY 4.0
spelling	Power comparisons between AntEpiSeeker(A), DECMDR(D), HS-MMGKG(G), SEE(S), SHEIB-AGM(B), SNPHarvester(H), SNPRuler(R) and Epi-SSA(P) on the DNME 1000 dataset.Liyan Sun (760586)Jingwen Bian (19942771)Yi Xin (146413)Linqing Jiang (19942774)Linxuan Zheng (19942777)BiochemistryGeneticsMolecular BiologyCancerComputational BiologyBiological Sciences not elsewhere classifiedMathematical Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedsparrow search algorithmmultiple objective functionsexperimental results demonstratecomparative toxicogenomics databasealongside 800 casealgorithms &# 8217800 control samplesdetecting epistatic interactionsssa draws inspirationssa algorithm outperformsexploring complex diseasesxlink "> genomeuncovering numerous genesorder epistatic interactionshighest average fdata set increasesseven complex diseasesepistasis detection tasksdnme 100 datasetscomplex diseasesxlink ">data increasesaverage fdetecting epistasisdnme 100five datasetsdnme 1000order epistasisepistasis risesdnme3 100dme 100wtccc datasetsource codesix thirdsignificant rolerelatedly reportedproperties consistentpotent toolpopulation basedotherwise parallelingmight playmarginal effectsidentifying higherhigher levelgene pairsdme 1000detect highctd ).crucial factorcomprehensive comparisonbetter utilizedaids us6sqwj /</<p>Power comparisons between AntEpiSeeker(A), DECMDR(D), HS-MMGKG(G), SEE(S), SHEIB-AGM(B), SNPHarvester(H), SNPRuler(R) and Epi-SSA(P) on the DNME 1000 dataset.</p>2024-10-24T17:48:50ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.1371/journal.pone.0311223.s036https://figshare.com/articles/dataset/Power_comparisons_between_AntEpiSeeker_A_DECMDR_D_HS-MMGKG_G_SEE_S_SHEIB-AGM_B_SNPHarvester_H_SNPRuler_R_and_Epi-SSA_P_on_the_DNME_1000_dataset_/27295435CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/272954352024-10-24T17:48:50Z
spellingShingle	Power comparisons between AntEpiSeeker(A), DECMDR(D), HS-MMGKG(G), SEE(S), SHEIB-AGM(B), SNPHarvester(H), SNPRuler(R) and Epi-SSA(P) on the DNME 1000 dataset. Liyan Sun (760586) Biochemistry Genetics Molecular Biology Cancer Computational Biology Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified sparrow search algorithm multiple objective functions experimental results demonstrate comparative toxicogenomics database alongside 800 case algorithms &# 8217 800 control samples detecting epistatic interactions ssa draws inspiration ssa algorithm outperforms exploring complex diseases xlink "> genome uncovering numerous genes order epistatic interactions highest average f data set increases seven complex diseases epistasis detection tasks dnme 100 datasets complex diseases xlink "> data increases average f detecting epistasis dnme 100 five datasets dnme 1000 order epistasis epistasis rises dnme3 100 dme 100 wtccc dataset source code six third significant role relatedly reported properties consistent potent tool population based otherwise paralleling might play marginal effects identifying higher higher level gene pairs dme 1000 detect high ctd ). crucial factor comprehensive comparison better utilized aids us 6sqwj /</
status_str	publishedVersion
title	Power comparisons between AntEpiSeeker(A), DECMDR(D), HS-MMGKG(G), SEE(S), SHEIB-AGM(B), SNPHarvester(H), SNPRuler(R) and Epi-SSA(P) on the DNME 1000 dataset.
title_full	Power comparisons between AntEpiSeeker(A), DECMDR(D), HS-MMGKG(G), SEE(S), SHEIB-AGM(B), SNPHarvester(H), SNPRuler(R) and Epi-SSA(P) on the DNME 1000 dataset.
title_fullStr	Power comparisons between AntEpiSeeker(A), DECMDR(D), HS-MMGKG(G), SEE(S), SHEIB-AGM(B), SNPHarvester(H), SNPRuler(R) and Epi-SSA(P) on the DNME 1000 dataset.
title_full_unstemmed	Power comparisons between AntEpiSeeker(A), DECMDR(D), HS-MMGKG(G), SEE(S), SHEIB-AGM(B), SNPHarvester(H), SNPRuler(R) and Epi-SSA(P) on the DNME 1000 dataset.
title_short	Power comparisons between AntEpiSeeker(A), DECMDR(D), HS-MMGKG(G), SEE(S), SHEIB-AGM(B), SNPHarvester(H), SNPRuler(R) and Epi-SSA(P) on the DNME 1000 dataset.
title_sort	Power comparisons between AntEpiSeeker(A), DECMDR(D), HS-MMGKG(G), SEE(S), SHEIB-AGM(B), SNPHarvester(H), SNPRuler(R) and Epi-SSA(P) on the DNME 1000 dataset.
topic	Biochemistry Genetics Molecular Biology Cancer Computational Biology Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified sparrow search algorithm multiple objective functions experimental results demonstrate comparative toxicogenomics database alongside 800 case algorithms &# 8217 800 control samples detecting epistatic interactions ssa draws inspiration ssa algorithm outperforms exploring complex diseases xlink "> genome uncovering numerous genes order epistatic interactions highest average f data set increases seven complex diseases epistasis detection tasks dnme 100 datasets complex diseases xlink "> data increases average f detecting epistasis dnme 100 five datasets dnme 1000 order epistasis epistasis rises dnme3 100 dme 100 wtccc dataset source code six third significant role relatedly reported properties consistent potent tool population based otherwise paralleling might play marginal effects identifying higher higher level gene pairs dme 1000 detect high ctd ). crucial factor comprehensive comparison better utilized aids us 6sqwj /</

Power comparisons between AntEpiSeeker(A), DECMDR(D), HS-MMGKG(G), SEE(S), SHEIB-AGM(B), SNPHarvester(H), SNPRuler(R) and Epi-SSA(P) on the DNME 1000 dataset.

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