Hybridization of Moth flame optimization algorithm and quantum computing for gene selection in microarray data

Ever-increasing data in various fields like Bioinformatics field, which has led to the need to find a way to reduce the data dimensionality. Gene selection problem has a large number of genes (relevant, redundant or noise), which needs an effective method to help us in detecting diseases and cancer....

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	Journal of ambient intelligence and humanized computing Ročník 12; číslo 2; s. 2731 - 2750
Hlavní autoři:	Dabba, Ali, Tari, Abdelkamel, Meftali, Samy
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2021 Springer Nature B.V Springer
Témata:	Accuracy Algorithms Artificial Intelligence Bioinformatics Cancer Classification Computational Intelligence Computer Science Computers Engineering Feature selection Gene expression Genes Genetic algorithms Genomics Neural networks Optimization Optimization algorithms Original Research Quantum computing Redundancy Robotics and Automation Support vector machines Swarm intelligence User Interfaces and Human Computer Interaction Feature selection Quantum computing Microarray data Bio-inspired algorithms Optimization algorithms Swarm intelligence Moth flame optimization algorithm Evolutionary algorithms Gene expression Molecular biology Cancer classification
ISSN:	1868-5137, 1868-5145
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Abstract	Ever-increasing data in various fields like Bioinformatics field, which has led to the need to find a way to reduce the data dimensionality. Gene selection problem has a large number of genes (relevant, redundant or noise), which needs an effective method to help us in detecting diseases and cancer. In this problem, computational complexity is reduced by selecting a small number of genes, but it is necessary to choose the relevant genes to keep a high level of accuracy. Therefore, in order to find the optimal gene subset, it is essential to devise an effective exploration approach that can investigate a large number of possible gene subsets. In addition, it is required to use a powerful evaluation method to evaluate the relevance of these gene subsets. In this paper, we present a novel swarm intelligence algorithm for gene selection called quantum moth flame optimization algorithm (QMFOA), which based on hybridization between quantum computation and moth flame optimization (MFO) algorithm. The purpose of QMFOA is to identify a small gene subset that can be used to classify samples with high accuracy. The QMFOA has a simple two-phase approach, the first phase is a pre-processing that uses to address the difficulty of high-dimensional data, which measure the redundancy and the relevance of the gene, in order to obtain the relevant gene set. The second phase is a hybridization among MFOA, quantum computing, and support vector machine with leave-one-out cross-validation, etc., in order to solve the gene selection problem. We use quantum computing to guarantee a good trade-off between the exploration and the exploitation of the search space, while a new update moth operation using Hamming distance and Archimedes spiral allows an efficient exploration of all possible gene-subsets. The main objective of the second phase is to determine the best relevant gene subset of all genes obtained in the first phase. In order to assess the performance of the proposed QMFOA, we test QMFOA on thirteen microarray datasets (six binary-class and seven multi-class) to evaluate and compare the classification accuracy and the number of genes selected by the QMFOA against many recently published algorithms. Experimental results show that QMFOA provides greater classification accuracy and the ability to reduce the number of selected genes compared to the other algorithms.
AbstractList	Ever-increasing data in various fields like Bioinformatics field, which has led to the need to find a way to reduce the data dimensionality. Gene selection problem has a large number of genes (relevant, redundant or noise), which needs an effective method to help us in detecting diseases and cancer. In this problem, computational complexity is reduced by selecting a small number of genes, but it is necessary to choose the relevant genes to keep a high level of accuracy. Therefore, in order to find the optimal gene subset, it is essential to devise an effective exploration approach that can investigate a large number of possible gene subsets. In addition, it is required to use a powerful evaluation method to evaluate the relevance of these gene subsets. In this paper, we present a novel swarm intelligence algorithm for gene selection called quantum moth flame optimization algorithm (QMFOA), which based on hybridization between quantum computation and moth flame optimization (MFO) algorithm. The purpose of QMFOA is to identify a small gene subset that can be used to classify samples with high accuracy. The QMFOA has a simple two-phase approach, the first phase is a pre-processing that uses to address the difficulty of high-dimensional data, which measure the redundancy and the relevance of the gene, in order to obtain the relevant gene set. The second phase is a hybridization among MFOA, quantum computing, and support vector machine with leave-one-out cross-validation, etc., in order to solve the gene selection problem. We use quantum computing to guarantee a good trade-off between the exploration and the exploitation of the search space, while a new update moth operation using Hamming distance and Archimedes spiral allows an efficient exploration of all possible gene-subsets. The main objective of the second phase is to determine the best relevant gene subset of all genes obtained in the first phase. In order to assess the performance of the proposed QMFOA, we test QMFOA on thirteen microarray datasets (six binary-class and seven multi-class) to evaluate and compare the classification accuracy and the number of genes selected by the QMFOA against many recently published algorithms. Experimental results show that QMFOA provides greater classification accuracy and the ability to reduce the number of selected genes compared to the other algorithms.
Author	Tari, Abdelkamel Dabba, Ali Meftali, Samy
Author_xml	– sequence: 1 givenname: Ali orcidid: 0000-0001-5602-574X surname: Dabba fullname: Dabba, Ali email: ali.dabba@univ-msila.dz organization: Computer Science Department, Faculty of Mathematics and Computer Science, Mohamed Boudiaf University, Computer Science Department, Faculty of Sciences, Abderrahmane Mira University, Research center in Computer Science, Signal and Automatic Control of Lille-CRIStAL – sequence: 2 givenname: Abdelkamel surname: Tari fullname: Tari, Abdelkamel organization: Computer Science Department, Faculty of Sciences, Abderrahmane Mira University, Medical Computing Laboratory-LIMED – sequence: 3 givenname: Samy surname: Meftali fullname: Meftali, Samy organization: University of Lille, Research center in Computer Science, Signal and Automatic Control of Lille-CRIStAL
BackLink	https://hal.science/hal-03110399$$DView record in HAL
BookMark	eNp9kc1OAyEUhYnRRK2-gCsSVy5GYYCZsjRGrUmNG12TW4ZpMTNQgTGpTy_t-JO4kIRA4Hw3595zjPaddwahM0ouKSH1VaRlJcqClCRvzngh99ARnVbTQlAu9n_urD5EpzG-kryYZJTSI-Rmm0Wwjf2AZL3DvsWPPq1w20FvsF8n239_Qbf0waZVj8E1-G0Al4Yea9-vh2TdErc-4KVxBkfTGb1DrMO91cFDCLDBDSQ4QQctdNGcfp0T9HJ3-3wzK-ZP9w831_NCM8FSoaXklSCVEYwvKCnbmjel0EB0XcsSGNWsodpAK4EKWQlop3pB66ZujNSNMGyCLsa6K-jUOtgewkZ5sGp2PVfbN5Lbz0OQ7zRrz0ftOvi3wcSkXv0QXLanSkkl50RwmVXTUZX7iTGYVmmbdpNJAWynKFHbMNQYhsphqF0YaouWf9BvR_9CbIRiFrulCb-u_qE-AT2Fnys
CitedBy_id	crossref_primary_10_1007_s12652_022_04450_3 crossref_primary_10_3390_a14120358 crossref_primary_10_3390_s22197273 crossref_primary_10_1007_s10115_024_02282_5 crossref_primary_10_1109_ACCESS_2022_3221194 crossref_primary_10_1007_s12652_021_03441_0 crossref_primary_10_1016_j_patter_2025_101236 crossref_primary_10_1109_ACCESS_2024_3402652 crossref_primary_10_1007_s11042_023_16353_2 crossref_primary_10_1007_s42044_025_00252_w crossref_primary_10_1007_s42452_025_06638_6 crossref_primary_10_1016_j_heliyon_2025_e42544 crossref_primary_10_1016_j_eswa_2022_118946 crossref_primary_10_3390_ijms26157587 crossref_primary_10_1007_s11227_022_04507_2 crossref_primary_10_3390_en15093410 crossref_primary_10_3389_fdata_2025_1624507 crossref_primary_10_1007_s00521_024_09965_8 crossref_primary_10_3390_biomimetics8040332 crossref_primary_10_3390_math10101636 crossref_primary_10_1155_2021_6490118 crossref_primary_10_1007_s12652_022_04482_9 crossref_primary_10_1109_ACCESS_2023_3272556 crossref_primary_10_1177_17483026241232295 crossref_primary_10_3390_quantum7010009 crossref_primary_10_1016_j_chemolab_2023_104989 crossref_primary_10_1007_s12652_020_02692_7 crossref_primary_10_1109_ACCESS_2023_3346533 crossref_primary_10_1007_s00521_021_06775_0 crossref_primary_10_32604_cmc_2024_048146 crossref_primary_10_1007_s11831_022_09853_1 crossref_primary_10_1093_bib_bbae391 crossref_primary_10_1007_s00521_022_07780_7 crossref_primary_10_1016_j_asoc_2023_111141 crossref_primary_10_1007_s11128_025_04787_6 crossref_primary_10_1109_ACCESS_2022_3195044 crossref_primary_10_3390_a18030154 crossref_primary_10_1155_2022_9448199 crossref_primary_10_3390_biomedicines10082052 crossref_primary_10_1016_j_compbiomed_2023_107674 crossref_primary_10_1007_s11227_023_05643_z crossref_primary_10_1007_s11831_022_09801_z crossref_primary_10_1007_s12065_021_00669_9 crossref_primary_10_1016_j_asoc_2023_110649 crossref_primary_10_1007_s00521_022_07147_y
Cites_doi	10.1016/j.neucom.2016.09.078 10.1016/j.neucom.2013.12.012 10.1109/ICPR.1996.546751 10.1073/pnas.96.12.6745 10.1109/TSMC.1977.4309803 10.3390/computers5040024 10.1038/35005001 10.1049/cce:19990303 10.1007/s11042-018-6083-5 10.1007/11892960_117 10.1038/415436a 10.1073/pnas.191368598 10.1016/j.patcog.2005.01.012 10.1007/s10462-012-9330-6 10.1109/CEC.1999.785552 10.1109/ACCESS.2019.2919956 10.1119/1.1463744 10.1016/j.jbi.2016.05.007 10.1016/j.patrec.2006.08.016 10.1093/biomet/57.3.579 10.1016/j.eswa.2007.08.088 10.1016/j.patcog.2007.02.007 10.3233/IDA-1997-1302 10.1145/1569901.1569930 10.1142/S0129054112400217 10.1038/89044 10.1186/1755-8794-6-S1-S3 10.1109/72.298224 10.6004/jnccn.2005.0016 10.1016/j.neucom.2016.07.080 10.1186/bcr1018 10.1109/TPAMI.2005.159 10.1126/science.286.5439.531 10.1109/TEVC.2002.804320 10.1007/s11460-005-0014-8 10.1504/IJDMB.2017.084026 10.1186/s12859-018-2400-2 10.1186/s12920-018-0447-6 10.1198/016214502753479248 10.1038/ng765 10.1186/1748-7188-8-15 10.1073/pnas.191502998 10.1016/S0140-6736(02)07746-2 10.1016/j.knosys.2015.07.006 10.1109/ISEFS.2006.251144 10.1016/j.patrec.2017.01.004 10.1016/S1535-6108(02)00030-2 10.1071/WR99107 10.1016/j.neucom.2010.02.025 10.1016/j.compbiolchem.2015.03.001 10.1016/j.ygeno.2017.07.010
ContentType	Journal Article
Copyright	Springer-Verlag GmbH Germany, part of Springer Nature 2020 Springer-Verlag GmbH Germany, part of Springer Nature 2020. licence_http://creativecommons.org/publicdomain/zero
Copyright_xml	– notice: Springer-Verlag GmbH Germany, part of Springer Nature 2020 – notice: Springer-Verlag GmbH Germany, part of Springer Nature 2020. – notice: licence_http://creativecommons.org/publicdomain/zero
DBID	AAYXX CITATION 8FE 8FG AFKRA ARAPS AZQEC BENPR BGLVJ CCPQU DWQXO GNUQQ HCIFZ JQ2 K7- P5Z P62 PHGZM PHGZT PKEHL PQEST PQGLB PQQKQ PQUKI 1XC
DOI	10.1007/s12652-020-02434-9
DatabaseName	CrossRef ProQuest SciTech Collection ProQuest Technology Collection ProQuest Central UK/Ireland ProQuest SciTech Premium Collection Technology Collection Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Central ProQuest Technology Collection ProQuest One Community College ProQuest Central Korea ProQuest Central Student SciTech Collection (ProQuest) ProQuest Computer Science Collection Computer Science Database Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection ProQuest One Academic ProQuest One Academic (New) ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition Hyper Article en Ligne (HAL)
DatabaseTitle	CrossRef Advanced Technologies & Aerospace Collection Computer Science Database ProQuest Central Student Technology Collection ProQuest One Academic Middle East (New) ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Computer Science Collection ProQuest One Academic Eastern Edition SciTech Premium Collection ProQuest One Community College ProQuest Technology Collection ProQuest SciTech Collection ProQuest Central Advanced Technologies & Aerospace Database ProQuest One Applied & Life Sciences ProQuest One Academic UKI Edition ProQuest Central Korea ProQuest Central (New) ProQuest One Academic ProQuest One Academic (New)
DatabaseTitleList	Advanced Technologies & Aerospace Collection
Database_xml	– sequence: 1 dbid: P5Z name: AAdvanced Technologies & Aerospace Database (subscription) url: https://search.proquest.com/hightechjournals sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Engineering Computer Science
EISSN	1868-5145
EndPage	2750
ExternalDocumentID	oai:HAL:hal-03110399v1 10_1007_s12652_020_02434_9
GroupedDBID	-EM 06D 0R~ 0VY 1N0 203 29~ 2JY 2VQ 30V 4.4 406 408 409 40D 96X AACDK AAHNG AAIAL AAJBT AAJKR AANZL AARHV AARTL AASML AATNV AATVU AAUYE AAWCG AAYIU AAYQN AAYTO AAYZH AAZMS ABAKF ABBXA ABDZT ABECU ABFTV ABHQN ABJNI ABJOX ABKCH ABMQK ABQBU ABSXP ABTEG ABTHY ABTKH ABTMW ABULA ABWNU ABXPI ACAOD ACDTI ACGFS ACHSB ACKNC ACMLO ACOKC ACPIV ACZOJ ADHHG ADHIR ADINQ ADKNI ADKPE ADRFC ADTPH ADURQ ADYFF ADZKW AEBTG AEFQL AEGNC AEJHL AEJRE AEMSY AENEX AEOHA AEPYU AESKC AETCA AEVLU AEXYK AFBBN AFKRA AFLOW AFQWF AFWTZ AFZKB AGAYW AGDGC AGJBK AGMZJ AGQEE AGQMX AGRTI AGWZB AGYKE AHAVH AHBYD AHKAY AHSBF AHYZX AIAKS AIGIU AIIXL AILAN AITGF AJBLW AJRNO AJZVZ AKLTO ALFXC ALMA_UNASSIGNED_HOLDINGS AMKLP AMXSW AMYLF AMYQR ANMIH ARAPS AUKKA AXYYD AYJHY BENPR BGLVJ BGNMA BSONS CCPQU CSCUP DNIVK DPUIP EBLON EBS EIOEI EJD ESBYG F5P FERAY FFXSO FIGPU FINBP FNLPD FRRFC FSGXE FYJPI GGCAI GGRSB GJIRD GQ6 GQ7 GQ8 H13 HCIFZ HF~ HG6 HMJXF HQYDN HRMNR HZ~ I0C IKXTQ IWAJR IXD IZIGR J-C J0Z JBSCW JCJTX JZLTJ K7- KOV LLZTM M4Y NPVJJ NQJWS NU0 O9- O93 O9J P2P P9P PT4 QOS R89 R9I RLLFE ROL RSV S1Z S27 S3B SEG SHX SISQX SJYHP SNE SNPRN SNX SOHCF SOJ SPISZ SRMVM SSLCW STPWE T13 TSG U2A UG4 UOJIU UTJUX UZXMN VFIZW W48 WK8 Z45 Z5O Z7R Z7X Z83 Z88 ZMTXR ~A9 AAYXX ABBRH ABDBE ABFSG ABRTQ ACSTC ADKFA AEZWR AFDZB AFFHD AFHIU AFOHR AHPBZ AHWEU AIXLP ATHPR AYFIA CITATION PHGZM PHGZT PQGLB 8FE 8FG AZQEC DWQXO GNUQQ JQ2 P62 PKEHL PQEST PQQKQ PQUKI 1XC
ID	FETCH-LOGICAL-c353t-c9946506e534b102f74d25ca0c7792a31c3d1ceaf9a15965af8cb17d7de9cd5e3
IEDL.DBID	RSV
ISICitedReferencesCount	47
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000556653300004&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	1868-5137
IngestDate	Wed Nov 26 07:06:43 EST 2025 Thu Nov 20 00:43:48 EST 2025 Sat Nov 29 03:41:26 EST 2025 Tue Nov 18 21:39:31 EST 2025 Fri Feb 21 02:49:22 EST 2025
IsPeerReviewed	true
IsScholarly	true
Issue	2
Keywords	Feature selection Quantum computing Microarray data Bio-inspired algorithms Optimization algorithms Swarm intelligence Moth flame optimization algorithm Evolutionary algorithms Gene expression Molecular biology Cancer classification
Language	English
License	licence_http://creativecommons.org/publicdomain/zero/: http://creativecommons.org/publicdomain/zero
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c353t-c9946506e534b102f74d25ca0c7792a31c3d1ceaf9a15965af8cb17d7de9cd5e3
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ORCID	0000-0001-5602-574X
PQID	2919440549
PQPubID	2043913
PageCount	20
ParticipantIDs	hal_primary_oai_HAL_hal_03110399v1 proquest_journals_2919440549 crossref_citationtrail_10_1007_s12652_020_02434_9 crossref_primary_10_1007_s12652_020_02434_9 springer_journals_10_1007_s12652_020_02434_9
PublicationCentury	2000
PublicationDate	2021-02-01
PublicationDateYYYYMMDD	2021-02-01
PublicationDate_xml	– month: 02 year: 2021 text: 2021-02-01 day: 01
PublicationDecade	2020
PublicationPlace	Berlin/Heidelberg
PublicationPlace_xml	– name: Berlin/Heidelberg – name: Heidelberg
PublicationTitle	Journal of ambient intelligence and humanized computing
PublicationTitleAbbrev	J Ambient Intell Human Comput
PublicationYear	2021
Publisher	Springer Berlin Heidelberg Springer Nature B.V Springer
Publisher_xml	– name: Springer Berlin Heidelberg – name: Springer Nature B.V – name: Springer
References	Zhu, Ong, Dash (CR66) 2007; 40 Alanni, Hou, Azzawi, Xiang (CR2) 2019; 12 Mundra, Rajapakse (CR44) 2010; 73 Das, Das (CR16) 2017; 88 Han, Kim (CR29) 2002; 6 Battiti (CR7) 1994; 5 Staunton, Slonim, Coller, Tamayo, Angelo, Park, Scherf, Lee, Reinhold, Weinstein (CR55) 2001; 98 CR32 CR31 Mohamad, Omatu, Deris, Yoshioka, Abdullah, Ibrahim (CR42) 2013; 8 Lahoz-Beltra (CR36) 2016; 5 Lu, Chen, Yan, Jin, Xue, Gao (CR39) 2017; 256 Chen, Zhang, Gutman (CR13) 2016; 62 Bennett, DiVincenzo (CR8) 2000; 404 Dudoit, Fridlyand, Speed (CR22) 2002; 97 Jeffrey, Lønning, Hillner (CR34) 2005; 3 Armstrong, Staunton, Silverman, Pieters, den Boer, Minden, Sallan, Lander, Golub, Korsmeyer (CR5) 2001; 30 Alshamlan, Badr, Alohali (CR4) 2015; 56 Breslow (CR10) 1970; 57 Burnham, Anderson (CR11) 2001; 28 Tahir, Bouridane, Kurugollu (CR57) 2007; 28 Mirjalili (CR41) 2015; 89 CR48 CR46 CR45 Dash, Liu (CR17) 1997; 1 Dashtban, Balafar, Suravajhala (CR18) 2018; 110 Sardana, Agrawal, Kaur (CR53) 2016; 20 Aziz, Verma, Jha, Srivastava (CR6) 2017; 17 Mudaliar, Patil, Thete, Moholkar (CR43) 2015; 2 Wang, Zhang, Dai, Chen, Yuan (CR60) 2013; 6 Frank, Rich, Longcore (CR24) 2006; 20 Nutt, Mani, Betensky, Tamayo, Cairncross, Ladd, Pohl, Hartmann, McLaughlin, Batchelor (CR47) 2003; 63 Du, Li, Li, Fei (CR21) 2014; 133 Su, Welsh, Sapinoso, Kern, Dimitrov, Lapp, Schultz, Powell, Moskaluk, Frierson (CR56) 2001; 61 CR19 Chang, Hilsenbeck, Fuqua (CR12) 2005; 7 Liu, Xu, Zhang, Liu, Yu, Liu, Dehmer (CR38) 2018; 19 Singh, Febbo, Ross, Jackson, Manola, Ladd, Tamayo, Renshaw, D’Amico, Richie (CR54) 2002; 1 Cover, Van Campenhout (CR15) 1977; 7 Golub, Slonim, Tamayo, Huard, Gaasenbeek, Mesirov, Coller, Loh, Downing, Caligiuri (CR25) 1999; 286 Jain, Nandakumar, Ross (CR33) 2005; 38 Press, Teukolsky, Vetterling, Flannery (CR52) 1992 Hey (CR30) 1999; 10 Cover, Thomas (CR14) 1991; 2 Wang, Niu (CR59) 2017; 228 Abderrahim, Talbi, Khaled (CR1) 2012; 23 Pomeroy, Tamayo, Gaasenbeek, Sturla, Angelo, McLaughlin, Kim, Goumnerova, Black, Lau (CR51) 2002; 415 Torkkola (CR58) 2003; 3 Peng, Long, Ding (CR49) 2005; 20 Deng, Li, Weng, Zhang (CR20) 2019; 78 Khan, Wei, Ringner, Saal, Ladanyi, Westermann, Berthold, Schwab, Antonescu, Peterson (CR35) 2001; 7 Petricoin, Ardekani, Hitt, Levine, Fusaro, Steinberg, Mills, Simone, Fishman, Kohn (CR50) 2002; 359 Hameed, Muhammad, Hassan, Saeed (CR27) 2018; 14 Xi, Sun, Liu, Fan, Wu (CR64) 2016; 20 Lin, Ying, Chen, Lee (CR37) 2008; 35 Wu, Ma, Fan, Xu, Shen (CR63) 2019; 7 Alon, Barkai, Notterman, Gish, Ybarra, Mack, Levine (CR3) 1999; 96 CR28 CR26 CR23 Zhang, Li, Jin, Hu (CR65) 2006; 1 CR61 Manju, Nigam (CR40) 2014; 42 Wilcoxon (CR62) 1992 Bhattacharjee, Richards, Staunton, Li, Monti, Vasa, Ladd, Beheshti, Bueno, Gillette (CR9) 2001; 98 2434_CR26 2434_CR23 EF Petricoin III (2434_CR50) 2002; 359 AI Su (2434_CR56) 2001; 61 CL Nutt (2434_CR47) 2003; 63 2434_CR61 R Aziz (2434_CR6) 2017; 17 TM Cover (2434_CR15) 1977; 7 HM Alshamlan (2434_CR4) 2015; 56 S Mirjalili (2434_CR41) 2015; 89 D Du (2434_CR21) 2014; 133 Q Wu (2434_CR63) 2019; 7 JC Chang (2434_CR12) 2005; 7 U Alon (2434_CR3) 1999; 96 S Dudoit (2434_CR22) 2002; 97 2434_CR28 A Das (2434_CR16) 2017; 88 SS Jeffrey (2434_CR34) 2005; 3 A Manju (2434_CR40) 2014; 42 SA Armstrong (2434_CR5) 2001; 30 2434_CR32 2434_CR31 J Khan (2434_CR35) 2001; 7 CH Bennett (2434_CR8) 2000; 404 Z Zhu (2434_CR66) 2007; 40 PU Mudaliar (2434_CR43) 2015; 2 A Bhattacharjee (2434_CR9) 2001; 98 MS Mohamad (2434_CR42) 2013; 8 X Deng (2434_CR20) 2019; 78 2434_CR48 F Wilcoxon (2434_CR62) 1992 2434_CR46 N Breslow (2434_CR10) 1970; 57 KH Han (2434_CR29) 2002; 6 2434_CR45 M Sardana (2434_CR53) 2016; 20 R Alanni (2434_CR2) 2019; 12 D Singh (2434_CR54) 2002; 1 KD Frank (2434_CR24) 2006; 20 W Press (2434_CR52) 1992 T Hey (2434_CR30) 1999; 10 A Abderrahim (2434_CR1) 2012; 23 KP Burnham (2434_CR11) 2001; 28 S Liu (2434_CR38) 2018; 19 H Peng (2434_CR49) 2005; 20 M Xi (2434_CR64) 2016; 20 SW Lin (2434_CR37) 2008; 35 PA Mundra (2434_CR44) 2010; 73 SL Pomeroy (2434_CR51) 2002; 415 K Torkkola (2434_CR58) 2003; 3 H Lu (2434_CR39) 2017; 256 A Jain (2434_CR33) 2005; 38 M Dash (2434_CR17) 1997; 1 GX Zhang (2434_CR65) 2006; 1 SS Hameed (2434_CR27) 2018; 14 TM Cover (2434_CR14) 1991; 2 H Wang (2434_CR60) 2013; 6 H Chen (2434_CR13) 2016; 62 H Wang (2434_CR59) 2017; 228 R Battiti (2434_CR7) 1994; 5 MA Tahir (2434_CR57) 2007; 28 M Dashtban (2434_CR18) 2018; 110 2434_CR19 TR Golub (2434_CR25) 1999; 286 R Lahoz-Beltra (2434_CR36) 2016; 5 JE Staunton (2434_CR55) 2001; 98
References_xml	– ident: CR45 – volume: 6 start-page: S3 year: 2013 ident: CR60 article-title: Tsg: a new algorithm for binary and multi-class cancer classification and informative genes selection publication-title: BMC Med Genom – volume: 28 start-page: 438 year: 2007 end-page: 446 ident: CR57 article-title: Simultaneous feature selection and feature weighting using hybrid tabu search/k-nearest neighbor classifier publication-title: Pattern Recogn Lett – volume: 56 start-page: 49 year: 2015 end-page: 60 ident: CR4 article-title: Genetic bee colony (gbc) algorithm: a new gene selection method for microarray cancer classification publication-title: Comput Biol Chem – volume: 89 start-page: 228 year: 2015 end-page: 249 ident: CR41 article-title: Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm publication-title: Knowl-Based Syst – volume: 12 start-page: 10 year: 2019 ident: CR2 article-title: A novel gene selection algorithm for cancer classification using microarray datasets publication-title: BMC Med Genom – volume: 133 start-page: 446 year: 2014 end-page: 458 ident: CR21 article-title: A novel forward gene selection algorithm for microarray data publication-title: Neurocomputing – volume: 23 start-page: 431 year: 2012 end-page: 444 ident: CR1 article-title: Hybridization of genetic and quantum algorithm for gene selection and classification of microarray data publication-title: Int J Found Comput Sci – volume: 228 start-page: 176 year: 2017 end-page: 186 ident: CR59 article-title: A novel bacterial algorithm with randomness control for feature selection in classification publication-title: Neurocomputing – volume: 20 start-page: 2016 year: 2016 ident: CR64 article-title: Cancer feature selection and classification using a binary quantum-behaved particle swarm optimization and support vector machine publication-title: Comput Math Methods Med – volume: 7 start-page: 657 year: 1977 end-page: 661 ident: CR15 article-title: On the possible orderings in the measurement selection problem publication-title: IEEE Tans Syst Man Cybern – volume: 10 start-page: 105 year: 1999 end-page: 112 ident: CR30 article-title: Quantum computing: an introduction publication-title: Comput Control Eng J – volume: 1 start-page: 31 year: 2006 end-page: 36 ident: CR65 article-title: Novel quantum genetic algorithm and its applications publication-title: Front Electr Electron Eng China – volume: 88 start-page: 12 year: 2017 end-page: 19 ident: CR16 article-title: Feature weighting and selection with a pareto-optimal trade-off between relevancy and redundancy publication-title: Pattern Recogn Lett – ident: CR61 – volume: 61 start-page: 7388 year: 2001 end-page: 7393 ident: CR56 article-title: Molecular classification of human carcinomas by use of gene expression signatures publication-title: Cancer Res – volume: 20 start-page: 161 year: 2016 end-page: 173 ident: CR53 article-title: A hybrid of clustering and quantum genetic algorithm for relevant genes selection for cancer microarray data publication-title: Int J Knowl Based Intell Eng Syst – volume: 5 start-page: 537 year: 1994 end-page: 550 ident: CR7 article-title: Using mutual information for selecting features in supervised neural net learning publication-title: IEEE Trans Neural Netw – volume: 256 start-page: 56 year: 2017 end-page: 62 ident: CR39 article-title: A hybrid feature selection algorithm for gene expression data classification publication-title: Neurocomputing – volume: 7 start-page: 673 year: 2001 ident: CR35 article-title: Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks publication-title: Nat Med – volume: 63 start-page: 1602 year: 2003 end-page: 1607 ident: CR47 article-title: Gene expression-based classification of malignant gliomas correlates better with survival than histological classification publication-title: Cancer Res – volume: 8 start-page: 15 year: 2013 ident: CR42 article-title: An enhancement of binary particle swarm optimization for gene selection in classifying cancer classes publication-title: Algorithms Mol Biol – ident: CR46 – volume: 98 start-page: 13790 year: 2001 end-page: 13795 ident: CR9 article-title: Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses publication-title: Proc Nat Acad Sci – ident: CR19 – volume: 404 start-page: 247 year: 2000 ident: CR8 article-title: Quantum information and computation publication-title: Nature – volume: 5 start-page: 24 year: 2016 ident: CR36 article-title: Quantum genetic algorithms for computer scientists publication-title: Computers – volume: 73 start-page: 2353 year: 2010 end-page: 2362 ident: CR44 article-title: Gene and sample selection for cancer classification with support vectors based t-statistic publication-title: Neurocomputing – volume: 78 start-page: 3797 year: 2019 end-page: 3816 ident: CR20 article-title: Feature selection for text classification: a review publication-title: Multimed Tools Appl – volume: 415 start-page: 436 year: 2002 ident: CR51 article-title: Prediction of central nervous system embryonal tumour outcome based on gene expression publication-title: Nature – ident: CR32 – volume: 17 start-page: 42 year: 2017 end-page: 65 ident: CR6 article-title: Artificial neural network classification of microarray data using new hybrid gene selection method publication-title: Int J Data Min Bioinform – volume: 20 start-page: 1226 year: 2005 end-page: 1238 ident: CR49 article-title: Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy publication-title: IEEE Trans Pattern Anal Mach Intell – volume: 20 start-page: 305 year: 2006 end-page: 344 ident: CR24 article-title: Effects of artificial night lighting on moths publication-title: Ecol Conseq Artif Night Light – ident: CR26 – volume: 1 start-page: 131 year: 1997 end-page: 156 ident: CR17 article-title: Feature selection for classification publication-title: Intell Data Anal – volume: 62 start-page: 12 year: 2016 end-page: 20 ident: CR13 article-title: A kernel-based clustering method for gene selection with gene expression data publication-title: J Biomed Inform – start-page: 196 year: 1992 end-page: 202 ident: CR62 publication-title: Individual comparisons by ranking methods. Breakthroughs in statistics – volume: 6 start-page: 580 year: 2002 end-page: 593 ident: CR29 article-title: Quantum-inspired evolutionary algorithm for a class of combinatorial optimization publication-title: IEEE Trans Evol Comput – volume: 14 start-page: 868 year: 2018 end-page: 880 ident: CR27 article-title: Gene selection and classification in microarray datasets using a hybrid approach of pcc-bpso/ga with multi classifiers publication-title: JCS – volume: 42 start-page: 79 year: 2014 end-page: 156 ident: CR40 article-title: Applications of quantum inspired computational intelligence: a survey publication-title: Artif Intell Rev – volume: 28 start-page: 111 year: 2001 end-page: 119 ident: CR11 article-title: Kullback–leibler information as a basis for strong inference in ecological studies publication-title: Wildl Res – volume: 110 start-page: 10 year: 2018 end-page: 17 ident: CR18 article-title: Gene selection for tumor classification using a novel bio-inspired multi-objective approach publication-title: Genomics – volume: 38 start-page: 2270 year: 2005 end-page: 2285 ident: CR33 article-title: Score normalization in multimodal biometric systems publication-title: Pattern Recogn – volume: 2 start-page: 1 year: 1991 end-page: 55 ident: CR14 article-title: Entropy, relative entropy and mutual information publication-title: Elements Inf Theory – volume: 19 start-page: 396 year: 2018 ident: CR38 article-title: Feature selection of gene expression data for cancer classification using double rbf-kernels publication-title: BMC Bioinform – volume: 3 start-page: 291 year: 2005 end-page: 300 ident: CR34 article-title: Genomics-based prognosis and therapeutic prediction in breast cancer publication-title: J Natl Compr Cancer Netw – volume: 98 start-page: 10787 year: 2001 end-page: 10792 ident: CR55 article-title: Chemosensitivity prediction by transcriptional profiling publication-title: Proc Nat Acad Sci – volume: 286 start-page: 531 year: 1999 end-page: 537 ident: CR25 article-title: Molecular classification of cancer: class discovery and class prediction by gene expression monitoring publication-title: Science – volume: 30 start-page: 41 year: 2001 ident: CR5 article-title: Mll translocations specify a distinct gene expression profile that distinguishes a unique leukemia publication-title: Nat Genet – start-page: 55 year: 1992 end-page: 62 ident: CR52 publication-title: Numerical recipes – ident: CR23 – volume: 1 start-page: 203 year: 2002 end-page: 209 ident: CR54 article-title: Gene expression correlates of clinical prostate cancer behavior publication-title: Cancer Cell – volume: 96 start-page: 6745 year: 1999 end-page: 6750 ident: CR3 article-title: Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays publication-title: Proc Nat Acad Sci – volume: 7 start-page: 100 year: 2005 ident: CR12 article-title: The promise of microarrays in the management and treatment of breast cancer publication-title: Breast Cancer Res – volume: 97 start-page: 77 year: 2002 end-page: 87 ident: CR22 article-title: Comparison of discrimination methods for the classification of tumors using gene expression data publication-title: J Am Stat Assoc – ident: CR48 – volume: 359 start-page: 572 year: 2002 end-page: 577 ident: CR50 article-title: Use of proteomic patterns in serum to identify ovarian cancer publication-title: Lancet – volume: 3 start-page: 1415 year: 2003 end-page: 1438 ident: CR58 article-title: Feature extraction by non-parametric mutual information maximization publication-title: J Mach Learn Res – ident: CR31 – volume: 35 start-page: 1817 year: 2008 end-page: 1824 ident: CR37 article-title: Particle swarm optimization for parameter determination and feature selection of support vector machines publication-title: Expert Syst Appl – volume: 2 start-page: 494 year: 2015 end-page: 499 ident: CR43 article-title: A fast clustering based feature subset selection algorithm for high dimensional data publication-title: Int J Emerg Trend Eng Basic Sci – volume: 7 start-page: 80588 year: 2019 end-page: 80601 ident: CR63 article-title: A feature selection method based on hybrid improved binary quantum particle swarm optimization publication-title: IEEE Access – ident: CR28 – volume: 57 start-page: 579 year: 1970 end-page: 594 ident: CR10 article-title: A generalized Kruskal–Wallis test for comparing k samples subject to unequal patterns of censorship publication-title: Biometrika – volume: 40 start-page: 3236 year: 2007 end-page: 3248 ident: CR66 article-title: Markov blanket-embedded genetic algorithm for gene selection publication-title: Pattern Recogn – volume: 3 start-page: 1415 year: 2003 ident: 2434_CR58 publication-title: J Mach Learn Res – volume: 228 start-page: 176 year: 2017 ident: 2434_CR59 publication-title: Neurocomputing doi: 10.1016/j.neucom.2016.09.078 – volume: 133 start-page: 446 year: 2014 ident: 2434_CR21 publication-title: Neurocomputing doi: 10.1016/j.neucom.2013.12.012 – ident: 2434_CR26 doi: 10.1109/ICPR.1996.546751 – volume: 96 start-page: 6745 year: 1999 ident: 2434_CR3 publication-title: Proc Nat Acad Sci doi: 10.1073/pnas.96.12.6745 – volume: 7 start-page: 657 year: 1977 ident: 2434_CR15 publication-title: IEEE Tans Syst Man Cybern doi: 10.1109/TSMC.1977.4309803 – volume: 5 start-page: 24 year: 2016 ident: 2434_CR36 publication-title: Computers doi: 10.3390/computers5040024 – volume: 404 start-page: 247 year: 2000 ident: 2434_CR8 publication-title: Nature doi: 10.1038/35005001 – ident: 2434_CR28 – volume: 10 start-page: 105 year: 1999 ident: 2434_CR30 publication-title: Comput Control Eng J doi: 10.1049/cce:19990303 – volume: 78 start-page: 3797 year: 2019 ident: 2434_CR20 publication-title: Multimed Tools Appl doi: 10.1007/s11042-018-6083-5 – ident: 2434_CR32 doi: 10.1007/11892960_117 – volume: 415 start-page: 436 year: 2002 ident: 2434_CR51 publication-title: Nature doi: 10.1038/415436a – volume: 20 start-page: 161 year: 2016 ident: 2434_CR53 publication-title: Int J Knowl Based Intell Eng Syst – volume: 98 start-page: 10787 year: 2001 ident: 2434_CR55 publication-title: Proc Nat Acad Sci doi: 10.1073/pnas.191368598 – volume: 2 start-page: 1 year: 1991 ident: 2434_CR14 publication-title: Elements Inf Theory – volume: 38 start-page: 2270 year: 2005 ident: 2434_CR33 publication-title: Pattern Recogn doi: 10.1016/j.patcog.2005.01.012 – volume: 42 start-page: 79 year: 2014 ident: 2434_CR40 publication-title: Artif Intell Rev doi: 10.1007/s10462-012-9330-6 – ident: 2434_CR45 doi: 10.1109/CEC.1999.785552 – volume: 7 start-page: 80588 year: 2019 ident: 2434_CR63 publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2919956 – ident: 2434_CR46 doi: 10.1119/1.1463744 – volume: 62 start-page: 12 year: 2016 ident: 2434_CR13 publication-title: J Biomed Inform doi: 10.1016/j.jbi.2016.05.007 – start-page: 196 volume-title: Individual comparisons by ranking methods. Breakthroughs in statistics year: 1992 ident: 2434_CR62 – ident: 2434_CR19 – volume: 2 start-page: 494 year: 2015 ident: 2434_CR43 publication-title: Int J Emerg Trend Eng Basic Sci – volume: 28 start-page: 438 year: 2007 ident: 2434_CR57 publication-title: Pattern Recogn Lett doi: 10.1016/j.patrec.2006.08.016 – volume: 57 start-page: 579 year: 1970 ident: 2434_CR10 publication-title: Biometrika doi: 10.1093/biomet/57.3.579 – volume: 35 start-page: 1817 year: 2008 ident: 2434_CR37 publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2007.08.088 – volume: 40 start-page: 3236 year: 2007 ident: 2434_CR66 publication-title: Pattern Recogn doi: 10.1016/j.patcog.2007.02.007 – volume: 1 start-page: 131 year: 1997 ident: 2434_CR17 publication-title: Intell Data Anal doi: 10.3233/IDA-1997-1302 – ident: 2434_CR23 doi: 10.1145/1569901.1569930 – volume: 23 start-page: 431 year: 2012 ident: 2434_CR1 publication-title: Int J Found Comput Sci doi: 10.1142/S0129054112400217 – ident: 2434_CR48 – volume: 7 start-page: 673 year: 2001 ident: 2434_CR35 publication-title: Nat Med doi: 10.1038/89044 – volume: 6 start-page: S3 year: 2013 ident: 2434_CR60 publication-title: BMC Med Genom doi: 10.1186/1755-8794-6-S1-S3 – volume: 5 start-page: 537 year: 1994 ident: 2434_CR7 publication-title: IEEE Trans Neural Netw doi: 10.1109/72.298224 – volume: 61 start-page: 7388 year: 2001 ident: 2434_CR56 publication-title: Cancer Res – start-page: 55 volume-title: Numerical recipes year: 1992 ident: 2434_CR52 – volume: 20 start-page: 305 year: 2006 ident: 2434_CR24 publication-title: Ecol Conseq Artif Night Light – volume: 3 start-page: 291 year: 2005 ident: 2434_CR34 publication-title: J Natl Compr Cancer Netw doi: 10.6004/jnccn.2005.0016 – volume: 256 start-page: 56 year: 2017 ident: 2434_CR39 publication-title: Neurocomputing doi: 10.1016/j.neucom.2016.07.080 – volume: 7 start-page: 100 year: 2005 ident: 2434_CR12 publication-title: Breast Cancer Res doi: 10.1186/bcr1018 – volume: 20 start-page: 1226 year: 2005 ident: 2434_CR49 publication-title: IEEE Trans Pattern Anal Mach Intell doi: 10.1109/TPAMI.2005.159 – volume: 63 start-page: 1602 year: 2003 ident: 2434_CR47 publication-title: Cancer Res – volume: 14 start-page: 868 year: 2018 ident: 2434_CR27 publication-title: JCS – volume: 286 start-page: 531 year: 1999 ident: 2434_CR25 publication-title: Science doi: 10.1126/science.286.5439.531 – volume: 6 start-page: 580 year: 2002 ident: 2434_CR29 publication-title: IEEE Trans Evol Comput doi: 10.1109/TEVC.2002.804320 – volume: 1 start-page: 31 year: 2006 ident: 2434_CR65 publication-title: Front Electr Electron Eng China doi: 10.1007/s11460-005-0014-8 – volume: 17 start-page: 42 year: 2017 ident: 2434_CR6 publication-title: Int J Data Min Bioinform doi: 10.1504/IJDMB.2017.084026 – volume: 19 start-page: 396 year: 2018 ident: 2434_CR38 publication-title: BMC Bioinform doi: 10.1186/s12859-018-2400-2 – volume: 12 start-page: 10 year: 2019 ident: 2434_CR2 publication-title: BMC Med Genom doi: 10.1186/s12920-018-0447-6 – volume: 97 start-page: 77 year: 2002 ident: 2434_CR22 publication-title: J Am Stat Assoc doi: 10.1198/016214502753479248 – volume: 30 start-page: 41 year: 2001 ident: 2434_CR5 publication-title: Nat Genet doi: 10.1038/ng765 – volume: 8 start-page: 15 year: 2013 ident: 2434_CR42 publication-title: Algorithms Mol Biol doi: 10.1186/1748-7188-8-15 – volume: 98 start-page: 13790 year: 2001 ident: 2434_CR9 publication-title: Proc Nat Acad Sci doi: 10.1073/pnas.191502998 – volume: 359 start-page: 572 year: 2002 ident: 2434_CR50 publication-title: Lancet doi: 10.1016/S0140-6736(02)07746-2 – volume: 89 start-page: 228 year: 2015 ident: 2434_CR41 publication-title: Knowl-Based Syst doi: 10.1016/j.knosys.2015.07.006 – volume: 20 start-page: 2016 year: 2016 ident: 2434_CR64 publication-title: Comput Math Methods Med – ident: 2434_CR61 doi: 10.1109/ISEFS.2006.251144 – volume: 88 start-page: 12 year: 2017 ident: 2434_CR16 publication-title: Pattern Recogn Lett doi: 10.1016/j.patrec.2017.01.004 – volume: 1 start-page: 203 year: 2002 ident: 2434_CR54 publication-title: Cancer Cell doi: 10.1016/S1535-6108(02)00030-2 – volume: 28 start-page: 111 year: 2001 ident: 2434_CR11 publication-title: Wildl Res doi: 10.1071/WR99107 – ident: 2434_CR31 – volume: 73 start-page: 2353 year: 2010 ident: 2434_CR44 publication-title: Neurocomputing doi: 10.1016/j.neucom.2010.02.025 – volume: 56 start-page: 49 year: 2015 ident: 2434_CR4 publication-title: Comput Biol Chem doi: 10.1016/j.compbiolchem.2015.03.001 – volume: 110 start-page: 10 year: 2018 ident: 2434_CR18 publication-title: Genomics doi: 10.1016/j.ygeno.2017.07.010
SSID	ssj0000393111
Score	2.3967328
Snippet	Ever-increasing data in various fields like Bioinformatics field, which has led to the need to find a way to reduce the data dimensionality. Gene selection...
SourceID	hal proquest crossref springer
SourceType	Open Access Repository Aggregation Database Enrichment Source Index Database Publisher
StartPage	2731
SubjectTerms	Accuracy Algorithms Artificial Intelligence Bioinformatics Cancer Classification Computational Intelligence Computer Science Computers Engineering Feature selection Gene expression Genes Genetic algorithms Genomics Neural networks Optimization Optimization algorithms Original Research Quantum computing Redundancy Robotics and Automation Support vector machines Swarm intelligence User Interfaces and Human Computer Interaction
SummonAdditionalLinks	– databaseName: Computer Science Database dbid: K7- link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1LTxsxELZK2gMXaFUQoaGyEDdqNX7sOj4hhIoi8VAPIHFb-bWARDaQBxL_vjOON2mRyoXr-rHWfrP2Z3vmG0IOgtOytLBN9cH1mSq9Rw3ImoVYBmd0UUaf1PXP9eXl4ObG_M4HbtPsVtnOiWmiDmOPZ-Q_hYHtNrALZY4enxhmjcLb1ZxCY4185EJwtPMzzZZnLBh3ylMKXhSFZwWXOsfNLKLnRFkIhvsnlOVTzPyzNq3doWfkX7Tz1U1pWoBON9879M9kI1NPerywlS_kQ2y-kmb4gjFbORqTjmt6AdjRGgwl0jHMJ6O2yD7cQqezuxG1TaBPc4BkPqI-ZYWAAVNgvxSsMdJpSq2DTe4bOkKHPzuZ2BeKzqhb5Pr019XJkOUcDMzLQs6YN0YBiStjIZUDMlJrFUThbd9rbYSV3MvAfbS1sUCMysLWA--4DjpE40MR5TbpNOMm7hCKZEtqh7G0QfWjGkjntHJRaKutknWX8PbrVz4LlGOejIdqJa2MiFWAWJUQq0yXHC7bPC7kOd6svQ-gLiuisvbw-LzCZzC34aW4eeZd0mtRrPL_PK1WEHbJj9YOVsX_f-Xu2719I-sCnWSSG3iPdGaTedwjn_zz7H46-Z6s-Q_aLvm7 priority: 102 providerName: ProQuest
Title	Hybridization of Moth flame optimization algorithm and quantum computing for gene selection in microarray data
URI	https://link.springer.com/article/10.1007/s12652-020-02434-9 https://www.proquest.com/docview/2919440549 https://hal.science/hal-03110399
Volume	12
WOSCitedRecordID	wos000556653300004&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVPQU databaseName: AAdvanced Technologies & Aerospace Database (subscription) customDbUrl: eissn: 1868-5145 dateEnd: 20241209 omitProxy: false ssIdentifier: ssj0000393111 issn: 1868-5137 databaseCode: P5Z dateStart: 20100301 isFulltext: true titleUrlDefault: https://search.proquest.com/hightechjournals providerName: ProQuest – providerCode: PRVPQU databaseName: Computer Science Database customDbUrl: eissn: 1868-5145 dateEnd: 20241209 omitProxy: false ssIdentifier: ssj0000393111 issn: 1868-5137 databaseCode: K7- dateStart: 20100301 isFulltext: true titleUrlDefault: http://search.proquest.com/compscijour providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 1868-5145 dateEnd: 20241209 omitProxy: false ssIdentifier: ssj0000393111 issn: 1868-5137 databaseCode: BENPR dateStart: 20100301 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVAVX databaseName: SpringerLink Journals customDbUrl: eissn: 1868-5145 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000393111 issn: 1868-5137 databaseCode: RSV dateStart: 20100301 isFulltext: true titleUrlDefault: https://link.springer.com/search?facet-content-type=%22Journal%22 providerName: Springer Nature
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1bb9MwFD6iGw_wwB1RGJWFeANLTWzH9eNAmyoxqmpcNPES-RZWaU23tJ20f885XtIOBEjwkofEdiKfY_tzfL7vALwOTovC4jbVBzfksvCeNCArHmIRnNGqiD6p6x_pyWR0cmKmLSls2UW7d0eSaabekt3yQuWctjukoie56cEuLncjGo7Hn75u_qwQ2zRLiXdJCp6rTOiWLfP7Zn5akXqnFA95A2z-cj6alp3D-__3wQ_gXgsz2f61XzyEW7F-BHdviA8-hnp8RWytlofJFhX7iFZjFbpIZAucSebdI3v2fdHMVqdzZuvALtZojPWc-ZQPAptiiHsZ-mFky5RUh6rMajanUD_bNPaKURjqE_hyePD5_Zi32Re4F0qsuDdGInwrohLSIQyptAy58nbotTa5FZkXIfPRVsYiJCqUrUbeZTroEI0PKoqnsFMv6vgMGMEsoR2xaIMcRjkSzmnpYq6ttlJUfcg6C5S-lSanDBln5VZUmfqyxL4sU1-Wpg9vNnXOr4U5_lr6FRp2U5A0tcf7RyXdw1mNjsPNZdaHvc7uZTuSl2VuMiMR1Ups421n5-3jP7_y-b8VfwF3cgqXSQHhe7CzatbxJdz2l6vZshnA7ruDyfR4AL0PmuN1qr4Nktf_AL9N99s
linkProvider	Springer Nature
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1JbxMxFH5qCxJcWASIQAELwQlGZGzPOD4gVAFVqqZRD0WquBhvQys1kzZLUf4Uv5H3nJkEkOitB67jZWT7e5v9FoBXwSlRWjRTfXDdTJbeUw7IKguxDE6roow-ZdcfqOGwd3ysDzfgZxsLQ26VLU9MjDqMPd2Rv-MazW3ULqT-cH6RUdUoel1tS2gsYbEfFz_QZJu-3_uE5_ua893PRx_7WVNVIPOiELPMay1RLSljIaRD8VopGXjhbdcrpbkVuRch99FW2qKoLwtb9bzLVVAhah-KKHDeTbghJRpLSD-HxdfVnQ7Fueap5C8loc-KXKgmTmcZrcfLgmdkr1EaQJnpP2Th5gl5Yv6m5v71MpsE3u7d_22r7sGdRrVmO0tauA8bsX4AdX9BMWlNtCkbV-wAsckqJITIxsgvR22TPfuOi5idjJitA7uYI-TmI-ZT1QvcIIbaPUNqi2yaSgfRkNOajcih0U4mdsHI2fYhfLmWJT6CrXpcx8fASJkUylGscJDdKHvCOSVd5MoqK0XVgbw9beObBOxUB-TMrFNHE0IMIsQkhBjdgTerMefL9CNX9n6JIFp1pMzh_Z2BoW_Iu-nRX1_mHdhuUWMafjU1a8h04G2Lu3Xzv3_55OrZXsCt_tHBwAz2hvtP4TYnh6Dk8r4NW7PJPD6Dm_5ydjqdPE-UxODbdePxF5PcV-4
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1bT9swFD4abELjgV1gohsbFuJtRDSxE9ePCKg60VVIbBNvlm-BSjSFNEXi3-PjJm03sUkTr_Elkc-J_dk-33cA9q3mNFN-m2qsbkcsMwY1IPPIusxqwdPMmaCu3-eDQefyUpwvsfhDtHtzJTnjNKBKU1Ed3tr8cEF8S7I0iXDrg4p6LBIr8JJh0iDcr1_8mp-yIPM0Dkl4URY-SmPKa-bM0938tjqtXGNs5BLw_OOuNCxB3TfP__i3sFHDT3I085d38MIV72F9SZRwE4reA7K4an4mGefku7cmyb3rODL2M8yoKVI3V-NyWF2PiCosuZt6I01HxIQ8Eb4r4vEw8f7pyCQk28Emw4KMMARQlaV6IBieugU_u6c_jntRnZUhMjSlVWSEYB7WZS6lTHt4knNmk9SotuFcJIrGhtrYOJUL5aFSlqq8Y3TMLbdOGJs6-gFWi3HhtoEg_KJcI7vWsrZjHao1Z9olXHHFaN6CuLGGNLVkOWbOuJELsWUcS-nHUoaxlKIFX-dtbmeCHf-sveeNPK-IWtu9o77EZ362w2tycR-3YKfxAVn_4ROZiFgwj3aZ7-Ogsfmi-O-v_Ph_1Xdh7fykK_vfBmef4HWCETUhZnwHVqty6j7DK3NfDSfll-D4j-B-AEg
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Hybridization+of+Moth+flame+optimization+algorithm+and+quantum+computing+for+gene+selection+in+microarray+data&rft.jtitle=Journal+of+ambient+intelligence+and+humanized+computing&rft.au=Dabba%2C+Ali&rft.au=Tari%2C+Abdelkamel&rft.au=Meftali%2C+Samy&rft.date=2021-02-01&rft.pub=Springer+Berlin+Heidelberg&rft.issn=1868-5137&rft.eissn=1868-5145&rft.volume=12&rft.issue=2&rft.spage=2731&rft.epage=2750&rft_id=info:doi/10.1007%2Fs12652-020-02434-9&rft.externalDocID=10_1007_s12652_020_02434_9
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1868-5137&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1868-5137&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1868-5137&client=summon