Learning gene regulatory networks using the bees algorithm
Learning gene regulatory networks under the threshold Boolean network model is presented. To accomplish this, the swarm intelligence technique called the bees algorithm is formulated to learn networks with predefined attractors. The resulting technique is compared with simulated annealing through si...
Saved in:
| Published in | Neural computing & applications Vol. 22; no. 1; pp. 63 - 70 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
London
Springer-Verlag
01.01.2013
|
| Subjects | |
| Online Access | Get full text |
| ISSN | 0941-0643 1433-3058 1433-3058 |
| DOI | 10.1007/s00521-011-0750-z |
Cover
| Abstract | Learning gene regulatory networks under the threshold Boolean network model is presented. To accomplish this, the swarm intelligence technique called the bees algorithm is formulated to learn networks with predefined attractors. The resulting technique is compared with simulated annealing through simulations. The ability of the networks to preserve the attractors when the updating schemes is changed from parallel to sequential is analyzed as well. Results show that Boolean networks are not very robust when the updating scheme is changed. Robust networks were found only for limit cycle length equal to two and specific network topologies. Throughout the simulations, the bees algorithm outperformed simulated annealing, showing the effectiveness of this swarm intelligence technique for this particular application. |
|---|---|
| AbstractList | Learning gene regulatory networks under the threshold Boolean network model is presented. To accomplish this, the swarm intelligence technique called the bees algorithm is formulated to learn networks with predefined attractors. The resulting technique is compared with simulated annealing through simulations. The ability of the networks to preserve the attractors when the updating schemes is changed from parallel to sequential is analyzed as well. Results show that Boolean networks are not very robust when the updating scheme is changed. Robust networks were found only for limit cycle length equal to two and specific network topologies. Throughout the simulations, the bees algorithm outperformed simulated annealing, showing the effectiveness of this swarm intelligence technique for this particular application. |
| Author | Ruz, Gonzalo A. Goles, Eric |
| Author_xml | – sequence: 1 givenname: Gonzalo A. surname: Ruz fullname: Ruz, Gonzalo A. email: gonzalo.ruz@uai.cl organization: Facultad de Ingeniería y Ciencias, Universidad Adolfo Ibáñez – sequence: 2 givenname: Eric surname: Goles fullname: Goles, Eric organization: Facultad de Ingeniería y Ciencias, Universidad Adolfo Ibáñez |
| BookMark | eNqNkMtOwzAQRS1UJNrCB7DLDwTGcZwHO1TxkiqxgbU18SNNSZ3KdlS1X4-rdsWiYjGaxdWZmTMzMrGD1YTcU3igAOWjB-AZTYHGKjmkhysypTljKQNeTcgU6jwmRc5uyMz7NQDkRcWn5Gmp0dnOtkmrrU6cbscew-D2idVhN7gfn4z-GIeVThqtfYJ9O7gurDa35Npg7_Xduc_J9-vL1-I9XX6-fSyel6lkjIZUZoWEElCi4sZkqqwUopLK1HXOsWgyqfIGVC25qSqaS86MUVhgpBqqgLI5yU5zR7vF_Q77Xmxdt0G3FxTE0V6c7EW0F0d7cYgQPUHSDd47bf7FlH8Y2QUM3WCDw66_SJ5P9HGLbbUT62F0Nn7lAvQLUeSCoQ |
| CitedBy_id | crossref_primary_10_1007_s00500_018_3010_7 crossref_primary_10_1007_s00500_022_07043_6 crossref_primary_10_1007_s00521_013_1528_2 crossref_primary_10_1016_j_biosystems_2013_10_007 crossref_primary_10_1007_s00521_019_04229_2 crossref_primary_10_1007_s12559_015_9349_5 crossref_primary_10_1016_j_amc_2014_09_079 crossref_primary_10_1007_s11047_019_09730_0 crossref_primary_10_4018_IJALR_2018010101 crossref_primary_10_3390_a14020061 crossref_primary_10_1186_0717_6287_47_64 crossref_primary_10_1186_s12859_020_3472_3 crossref_primary_10_1016_j_neucom_2015_10_095 crossref_primary_10_1093_bioinformatics_btv628 crossref_primary_10_1080_10798587_2016_1196880 crossref_primary_10_1080_23311916_2015_1091540 |
| Cites_doi | 10.1109/ICMLA.2010.139 10.1016/S1672-0229(08)60026-1 10.1093/bioinformatics/bth448 10.1504/EJIE.2009.027035 10.1126/science.220.4598.671 10.1016/S1672-6529(08)60103-1 10.1016/j.jtbi.2010.04.012 10.1016/j.neunet.2007.07.002 10.1016/j.artmed.2009.11.001 10.1016/j.biosystems.2009.03.006 10.1186/1471-2164-10-S1-S15 10.1016/S0303-2647(02)00019-9 10.1016/j.aam.2010.03.002 10.1243/09544062JMES1494 10.1529/biophysj.106.083485 10.1007/978-3-540-85190-5_28 10.1016/B978-008045157-2/50081-X 10.1371/journal.pone.0011793 10.1007/11885191_9 10.1007/978-3-540-35866-4_29 10.1006/jtbi.1998.0701 10.1109/INDIN.2009.5195852 10.1016/0022-5193(69)90015-0 10.1186/1471-2105-11-59 10.1007/978-3-540-87527-7_33 10.1243/09544062JMES838 10.1016/j.tcs.2007.09.008 |
| ContentType | Journal Article |
| Copyright | Springer-Verlag London Limited 2011 |
| Copyright_xml | – notice: Springer-Verlag London Limited 2011 |
| DBID | AAYXX CITATION ADTOC UNPAY |
| DOI | 10.1007/s00521-011-0750-z |
| DatabaseName | CrossRef Unpaywall for CDI: Periodical Content Unpaywall |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| Database_xml | – sequence: 1 dbid: UNPAY name: Unpaywall url: https://proxy.k.utb.cz/login?url=https://unpaywall.org/ sourceTypes: Open Access Repository |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISSN | 1433-3058 |
| EndPage | 70 |
| ExternalDocumentID | oai:americanae.aecid.es:3272663 10_1007_s00521_011_0750_z |
| GroupedDBID | -4Z -59 -5G -BR -EM -Y2 -~C .4S .86 .DC .VR 06D 0R~ 0VY 123 1N0 1SB 2.D 203 28- 29N 2J2 2JN 2JY 2KG 2LR 2P1 2VQ 2~H 30V 4.4 406 408 409 40D 40E 53G 5QI 5VS 67Z 6NX 8FE 8FG 8TC 8UJ 95- 95. 95~ 96X AAAVM AABHQ AACDK AAHNG AAIAL AAJBT AAJKR AANZL AAOBN AARHV AARTL AASML AATNV AATVU AAUYE AAWCG AAYIU AAYQN AAYTO AAYZH ABAKF ABBBX ABBXA ABDBF ABDZT ABECU ABFTD ABFTV ABHLI ABHQN ABJNI ABJOX ABKCH ABKTR ABLJU ABMNI ABMQK ABNWP ABQBU ABQSL ABSXP ABTEG ABTHY ABTKH ABTMW ABULA ABWNU ABXPI ACAOD ACBXY ACDTI ACGFS ACHSB ACHXU ACKNC ACMDZ ACMLO ACOKC ACOMO ACPIV ACSNA ACUHS ACZOJ ADHHG ADHIR ADIMF ADINQ ADKNI ADKPE ADMLS ADRFC ADTPH ADURQ ADYFF ADZKW AEBTG AEFIE AEFQL AEGAL AEGNC AEJHL AEJRE AEKMD AEMSY AENEX AEOHA AEPYU AESKC AETLH AEVLU AEXYK AFBBN AFEXP AFGCZ AFKRA AFLOW AFQWF AFWTZ AFZKB AGAYW AGDGC AGGDS AGJBK AGMZJ AGQEE AGQMX AGRTI AGWIL AGWZB AGYKE AHAVH AHBYD AHKAY AHSBF AHYZX AIAKS AIGIU AIIXL AILAN AITGF AJBLW AJRNO AJZVZ ALMA_UNASSIGNED_HOLDINGS ALWAN AMKLP AMXSW AMYLF AMYQR AOCGG ARAPS ARCSS ARMRJ ASPBG AVWKF AXYYD AYJHY AZFZN B-. B0M BA0 BBWZM BDATZ BENPR BGLVJ BGNMA BSONS CAG CCPQU COF CS3 CSCUP DDRTE DL5 DNIVK DPUIP DU5 EAD EAP EBLON EBS ECS EDO EIOEI EJD EMI EMK EPL ESBYG EST ESX F5P FEDTE FERAY FFXSO FIGPU FINBP FNLPD FRRFC FSGXE FWDCC GGCAI GGRSB GJIRD GNWQR GQ6 GQ7 GQ8 GXS H13 HCIFZ HF~ HG5 HG6 HMJXF HQYDN HRMNR HVGLF HZ~ I-F I09 IHE IJ- IKXTQ ITM IWAJR IXC IZIGR IZQ I~X I~Z J-C J0Z JBSCW JCJTX JZLTJ KDC KOV KOW LAS LLZTM M4Y MA- N2Q N9A NB0 NDZJH NPVJJ NQJWS NU0 O9- O93 O9G O9I O9J OAM P19 P2P P62 P9O PF0 PT4 PT5 QOK QOS R4E R89 R9I RHV RIG RNI RNS ROL RPX RSV RZK S16 S1Z S26 S27 S28 S3B SAP SCJ SCLPG SCO SDH SDM SHX SISQX SJYHP SNE SNPRN SNX SOHCF SOJ SPISZ SRMVM SSLCW STPWE SZN T13 T16 TSG TSK TSV TUC TUS U2A UG4 UOJIU UTJUX UZXMN VC2 VFIZW W23 W48 WK8 YLTOR Z45 Z5O Z7R Z7S Z7V Z7W Z7X Z7Y Z7Z Z81 Z83 Z86 Z88 Z8M Z8N Z8P Z8Q Z8R Z8S Z8T Z8U Z8W Z92 ZMTXR ~8M ~EX AAPKM AAYXX ABBRH ABDBE ABFSG ABRTQ ACSTC ADHKG ADKFA AEZWR AFDZB AFHIU AFOHR AGQPQ AHPBZ AHWEU AIXLP ATHPR AYFIA CITATION PHGZM PHGZT PQGLB PUEGO ADTOC UNPAY |
| ID | FETCH-LOGICAL-c331t-c26c070acad5ff2d78daadcdf9945a6b2cd4b0d9c5f8814c53ffda6a6c0b1d013 |
| IEDL.DBID | U2A |
| ISSN | 0941-0643 1433-3058 |
| IngestDate | Sun Oct 26 04:14:16 EDT 2025 Wed Oct 01 02:25:36 EDT 2025 Thu Apr 24 23:01:44 EDT 2025 Fri Feb 21 02:34:27 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Keywords | Attractors Swarm intelligence The bees algorithm Boolean networks Simulated annealing |
| Language | English |
| License | http://www.springer.com/tdm other-oa |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c331t-c26c070acad5ff2d78daadcdf9945a6b2cd4b0d9c5f8814c53ffda6a6c0b1d013 |
| OpenAccessLink | https://proxy.k.utb.cz/login?url=http://americanae.aecid.es/americanae/es/registros/registro.do?tipoRegistro=MTD&idBib=3272663 |
| PageCount | 8 |
| ParticipantIDs | unpaywall_primary_10_1007_s00521_011_0750_z crossref_primary_10_1007_s00521_011_0750_z crossref_citationtrail_10_1007_s00521_011_0750_z springer_journals_10_1007_s00521_011_0750_z |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 20130100 2013-1-00 |
| PublicationDateYYYYMMDD | 2013-01-01 |
| PublicationDate_xml | – month: 1 year: 2013 text: 20130100 |
| PublicationDecade | 2010 |
| PublicationPlace | London |
| PublicationPlace_xml | – name: London |
| PublicationTitle | Neural computing & applications |
| PublicationTitleAbbrev | Neural Comput & Applic |
| PublicationYear | 2013 |
| Publisher | Springer-Verlag |
| Publisher_xml | – name: Springer-Verlag |
| References | Ruz GA, Goles E (2010b) Learning gene regulatory networks with predefined attractors for sequential updating schemes using simulated annealing. In: Proceeding of IEEE the Ninth International Conference on Machine Learning and Applications (ICMLA 2010), pp 889–894 PhamDTCastellaniMThe bees algorithm: modelling foraging behaviour to solve continuous optimization problemsProc IMechE Part C: J Mech Eng Sci20092232919293810.1243/09544062JMES1494 XuRVenayagamoorthyGWunschDModeling of gene regulatory networks with hybrid differential evolution and particle swarm optimizationNeural Netw200720917927 YuJSmithVAWangPPHarteminkAJJarvisEDAdvances to Bayesian network inference for generating causal networks from observational biological dataBioinformatics2004203594360310.1093/bioinformatics/bth448 LeeWYangKApplying intelligent computing techniques to modeling biological networks from expression dataGenom Proteom Bioinform2008611112010.1016/S1672-0229(08)60026-1 ZhangYXuanJde los ReyesBClarkeRRessomHReverse engineering module networks by pso-rnn hybrid modelingBMC Genom200910S1510.1186/1471-2164-10-S1-S15 BaykasogluAOzbakirLTapkanPThe bees algorithm for workload balancing in examination job assignmentEu J Ind Eng2009342443510.1504/EJIE.2009.027035 PhamDTGhanbarzadehAOtriSKoEOptimal design of mechanical components using the bees algorithmProc Inst Mech Eng Part C: J Mech Eng Sci20092231051105610.1243/09544062JMES838 Liang S, Fuhrman S, Somogyi R (1998) Reveal, a general reverse engineering algorithm for inference of genetic network architectures. In: Pac Symp Biocomput, pp 18–29 Ang MC, Pham DT, Ng KW (2009) Minimum-time motion planning for a robot arm using the bees algorithm. In: 7th IEEE International Conference on Industrial Informatics, 2009. INDIN, pp 487–492 LiuGFengWWangHLiuLZhouCReconstruction of gene regulatory networks based on two-stage Bayesian network structure learning algorithmJ Bionic Eng20096869210.1016/S1672-6529(08)60103-1 GolesESalinasLComparison between parallel and serial dynamics of Boolean networksTheor Comput Sci200839624725324122581145.6803510.1016/j.tcs.2007.09.008 KauffmanSAMetabolic stability and epigenesis in randomly constructed genetic netsJ Theor Biol19692243746724616010.1016/0022-5193(69)90015-0 Akutsu T, Miyano S, Kuhara S (1999) Identification of genetic networks from a small number of gene expression patterns under the Boolean network model. In: Pac Symp Biocomput pp 17–28 ShinAIbaHConstruction of genetic network using evolutionary algorithm and combined fitness functionGenome Inform20031494103 KirkpatrickSGelattCDVecchiMPOptimization by simulated annealingScience19832206716807024851225.9016210.1126/science.220.4598.671 MendozaLAlvarez-BuyllaERDynamics of the genetic regulatory network for arabidopsis thaliana flower morphogenesisJ Theor Biol199819330731910.1006/jtbi.1998.0701 TomshineJKaznessisYNOptimization of a stochastically simulated gene network model via simulated annealingBiophys J2006913196320510.1529/biophysj.106.083485 XuRVenayagamoorthyGWunschDWangJYiZZuradaJLuBLYinHA study of particle swarm optimization in gene regulatory networks inferenceAdvances in neural networks—ISNN 2006, lecture notes in computer science, vol 3973.2006BerlinSpringer648653 GolesESalinasLSequential operator for filtering cycles in Boolean networksAdv Appl Math20104534635826690721205.6817810.1016/j.aam.2010.03.002 RepsilberDLiljenstromHAnderssonSGEReverse engineering of regulatory networks: simulation studies on a genetic algorithm approach for ranking hypothesesBiosystems200266314110.1016/S0303-2647(02)00019-9 SerraRVillaniMBarbieriAKauffmanSAColacciAOn the dynamics of random Boolean networks subject to noise: attractors, ergodic sets and cell typesJ Theor Biol201026518519310.1016/j.jtbi.2010.04.012 Ruz GA, Goles E (2010a) Cycle attractors for different deterministic updating schemes in Boolean regulation networks. In: Proc. of the IASTED International Conference on Computational Bioscience (Comp-Bio 2010), pp 620–625 HuangSMinaiAABar-YamYCell state dynamics and tumorigenesis in Boolean regulatory networksUnifying themes in complex systems2006BerlinSpringer29330510.1007/978-3-540-35866-4_29 KentzoglanakisKPooleMAdamsCDorigoMBirattariMBlumCClercMSttzleTWinfieldAIncorporating heuristics in a swarm intelligence framework for inferring gene regulatory networks from gene expression time seriesAnt colony optimization and swarm intelligence, lecture notes in computer science, vol 52172008HeidelbergSpringer Berlin32333010.1007/978-3-540-87527-7_33 Hoon MD, Imoto S, Miyano S (2003) Inferring gene regulatory networks from time-ordered gene expression data of bacillus subtilis using differential equations. In: Pac Symp Biocomput pp 17–28 AracenaJGolesEMoreiraASalinasLOn the robustness of update schedules in Boolean networksBiosystems2009971810.1016/j.biosystems.2009.03.006 LeeJYDarwishAHYooSDMulti-objective environmental/economic dispatch using the bees algorithm with weighted sumEKC2008 Proceedings of the EU-Korea conference on science and technology, Springer proceedings in physics, vol 124.2008HeidelbergSpringer Berlin26727410.1007/978-3-540-85190-5_28 Pham DT, Ghanbarzadeh A, Koc E, Otri S, Rahim S, Zaidi M (2006) The bees algorithm, a novel tool for complex optimisation problems. In: Proceedings of the Second International Virtual Conference on Intelligent production machines and systems (IPROMS 2006), pp 454–459 SirbuARuskinHCraneMComparison of evolutionary algorithms in gene regulatory network model inferenceBMC Bioinform2010115910.1186/1471-2105-11-59 DemongeotJGolesEMorvanMNoualMSenéSAttraction basins as gauges of robustness against boundary conditions in biological complex systemsPLoS ONE201058e11,79310.1371/journal.pone.0011793 StegglesLJBanksRWipatAModelling and analysing genetic networks: from Boolean networks to petri netsComput Methods Syst Biol20064210127141228834710.1007/11885191_9 ShmulevichIDoughertyERProbabilistic Boolean networks: the modeling and control of gene regulatory networks2009PhiladelphiaSIAM-Society for Industrial and Applied Mathematics ZhangSChingWTsingNLeungHGuoDA new multiple regression approach for the construction of genetic regulatory networksArtif Intell Med20104815316010.1016/j.artmed.2009.11.001 I Shmulevich (750_CR26) 2009 JY Lee (750_CR13) 2008 R Serra (750_CR24) 2010; 265 A Baykasoglu (750_CR4) 2009; 3 750_CR8 S Huang (750_CR9) 2006 LJ Steggles (750_CR28) 2006; 4210 J Yu (750_CR32) 2004; 20 750_CR1 J Aracena (750_CR3) 2009; 97 A Shin (750_CR25) 2003; 14 J Tomshine (750_CR29) 2006; 91 750_CR2 K Kentzoglanakis (750_CR11) 2008 J Demongeot (750_CR5) 2010; 5 750_CR31 A Sirbu (750_CR27) 2010; 11 S Kirkpatrick (750_CR12) 1983; 220 G Liu (750_CR16) 2009; 6 L Mendoza (750_CR17) 1998; 193 SA Kauffman (750_CR10) 1969; 22 DT Pham (750_CR20) 2009; 223 750_CR19 S Zhang (750_CR33) 2010; 48 750_CR15 DT Pham (750_CR18) 2009; 223 750_CR23 E Goles (750_CR7) 2010; 45 750_CR22 W Lee (750_CR14) 2008; 6 Y Zhang (750_CR34) 2009; 10 R Xu (750_CR30) 2006 D Repsilber (750_CR21) 2002; 66 E Goles (750_CR6) 2008; 396 |
| References_xml | – reference: SirbuARuskinHCraneMComparison of evolutionary algorithms in gene regulatory network model inferenceBMC Bioinform2010115910.1186/1471-2105-11-59 – reference: ZhangYXuanJde los ReyesBClarkeRRessomHReverse engineering module networks by pso-rnn hybrid modelingBMC Genom200910S1510.1186/1471-2164-10-S1-S15 – reference: Ang MC, Pham DT, Ng KW (2009) Minimum-time motion planning for a robot arm using the bees algorithm. In: 7th IEEE International Conference on Industrial Informatics, 2009. INDIN, pp 487–492 – reference: StegglesLJBanksRWipatAModelling and analysing genetic networks: from Boolean networks to petri netsComput Methods Syst Biol20064210127141228834710.1007/11885191_9 – reference: TomshineJKaznessisYNOptimization of a stochastically simulated gene network model via simulated annealingBiophys J2006913196320510.1529/biophysj.106.083485 – reference: Ruz GA, Goles E (2010b) Learning gene regulatory networks with predefined attractors for sequential updating schemes using simulated annealing. In: Proceeding of IEEE the Ninth International Conference on Machine Learning and Applications (ICMLA 2010), pp 889–894 – reference: Pham DT, Ghanbarzadeh A, Koc E, Otri S, Rahim S, Zaidi M (2006) The bees algorithm, a novel tool for complex optimisation problems. In: Proceedings of the Second International Virtual Conference on Intelligent production machines and systems (IPROMS 2006), pp 454–459 – reference: KauffmanSAMetabolic stability and epigenesis in randomly constructed genetic netsJ Theor Biol19692243746724616010.1016/0022-5193(69)90015-0 – reference: ShinAIbaHConstruction of genetic network using evolutionary algorithm and combined fitness functionGenome Inform20031494103 – reference: AracenaJGolesEMoreiraASalinasLOn the robustness of update schedules in Boolean networksBiosystems2009971810.1016/j.biosystems.2009.03.006 – reference: MendozaLAlvarez-BuyllaERDynamics of the genetic regulatory network for arabidopsis thaliana flower morphogenesisJ Theor Biol199819330731910.1006/jtbi.1998.0701 – reference: LiuGFengWWangHLiuLZhouCReconstruction of gene regulatory networks based on two-stage Bayesian network structure learning algorithmJ Bionic Eng20096869210.1016/S1672-6529(08)60103-1 – reference: YuJSmithVAWangPPHarteminkAJJarvisEDAdvances to Bayesian network inference for generating causal networks from observational biological dataBioinformatics2004203594360310.1093/bioinformatics/bth448 – reference: HuangSMinaiAABar-YamYCell state dynamics and tumorigenesis in Boolean regulatory networksUnifying themes in complex systems2006BerlinSpringer29330510.1007/978-3-540-35866-4_29 – reference: BaykasogluAOzbakirLTapkanPThe bees algorithm for workload balancing in examination job assignmentEu J Ind Eng2009342443510.1504/EJIE.2009.027035 – reference: Ruz GA, Goles E (2010a) Cycle attractors for different deterministic updating schemes in Boolean regulation networks. In: Proc. of the IASTED International Conference on Computational Bioscience (Comp-Bio 2010), pp 620–625 – reference: Liang S, Fuhrman S, Somogyi R (1998) Reveal, a general reverse engineering algorithm for inference of genetic network architectures. In: Pac Symp Biocomput, pp 18–29 – reference: SerraRVillaniMBarbieriAKauffmanSAColacciAOn the dynamics of random Boolean networks subject to noise: attractors, ergodic sets and cell typesJ Theor Biol201026518519310.1016/j.jtbi.2010.04.012 – reference: XuRVenayagamoorthyGWunschDModeling of gene regulatory networks with hybrid differential evolution and particle swarm optimizationNeural Netw200720917927 – reference: DemongeotJGolesEMorvanMNoualMSenéSAttraction basins as gauges of robustness against boundary conditions in biological complex systemsPLoS ONE201058e11,79310.1371/journal.pone.0011793 – reference: GolesESalinasLComparison between parallel and serial dynamics of Boolean networksTheor Comput Sci200839624725324122581145.6803510.1016/j.tcs.2007.09.008 – reference: ShmulevichIDoughertyERProbabilistic Boolean networks: the modeling and control of gene regulatory networks2009PhiladelphiaSIAM-Society for Industrial and Applied Mathematics – reference: XuRVenayagamoorthyGWunschDWangJYiZZuradaJLuBLYinHA study of particle swarm optimization in gene regulatory networks inferenceAdvances in neural networks—ISNN 2006, lecture notes in computer science, vol 3973.2006BerlinSpringer648653 – reference: PhamDTGhanbarzadehAOtriSKoEOptimal design of mechanical components using the bees algorithmProc Inst Mech Eng Part C: J Mech Eng Sci20092231051105610.1243/09544062JMES838 – reference: Akutsu T, Miyano S, Kuhara S (1999) Identification of genetic networks from a small number of gene expression patterns under the Boolean network model. In: Pac Symp Biocomput pp 17–28 – reference: KentzoglanakisKPooleMAdamsCDorigoMBirattariMBlumCClercMSttzleTWinfieldAIncorporating heuristics in a swarm intelligence framework for inferring gene regulatory networks from gene expression time seriesAnt colony optimization and swarm intelligence, lecture notes in computer science, vol 52172008HeidelbergSpringer Berlin32333010.1007/978-3-540-87527-7_33 – reference: GolesESalinasLSequential operator for filtering cycles in Boolean networksAdv Appl Math20104534635826690721205.6817810.1016/j.aam.2010.03.002 – reference: KirkpatrickSGelattCDVecchiMPOptimization by simulated annealingScience19832206716807024851225.9016210.1126/science.220.4598.671 – reference: PhamDTCastellaniMThe bees algorithm: modelling foraging behaviour to solve continuous optimization problemsProc IMechE Part C: J Mech Eng Sci20092232919293810.1243/09544062JMES1494 – reference: LeeWYangKApplying intelligent computing techniques to modeling biological networks from expression dataGenom Proteom Bioinform2008611112010.1016/S1672-0229(08)60026-1 – reference: RepsilberDLiljenstromHAnderssonSGEReverse engineering of regulatory networks: simulation studies on a genetic algorithm approach for ranking hypothesesBiosystems200266314110.1016/S0303-2647(02)00019-9 – reference: LeeJYDarwishAHYooSDMulti-objective environmental/economic dispatch using the bees algorithm with weighted sumEKC2008 Proceedings of the EU-Korea conference on science and technology, Springer proceedings in physics, vol 124.2008HeidelbergSpringer Berlin26727410.1007/978-3-540-85190-5_28 – reference: ZhangSChingWTsingNLeungHGuoDA new multiple regression approach for the construction of genetic regulatory networksArtif Intell Med20104815316010.1016/j.artmed.2009.11.001 – reference: Hoon MD, Imoto S, Miyano S (2003) Inferring gene regulatory networks from time-ordered gene expression data of bacillus subtilis using differential equations. In: Pac Symp Biocomput pp 17–28 – ident: 750_CR23 doi: 10.1109/ICMLA.2010.139 – volume: 6 start-page: 111 year: 2008 ident: 750_CR14 publication-title: Genom Proteom Bioinform doi: 10.1016/S1672-0229(08)60026-1 – ident: 750_CR8 – volume: 20 start-page: 3594 year: 2004 ident: 750_CR32 publication-title: Bioinformatics doi: 10.1093/bioinformatics/bth448 – ident: 750_CR1 – volume: 3 start-page: 424 year: 2009 ident: 750_CR4 publication-title: Eu J Ind Eng doi: 10.1504/EJIE.2009.027035 – volume: 220 start-page: 671 year: 1983 ident: 750_CR12 publication-title: Science doi: 10.1126/science.220.4598.671 – volume: 6 start-page: 86 year: 2009 ident: 750_CR16 publication-title: J Bionic Eng doi: 10.1016/S1672-6529(08)60103-1 – start-page: 648 volume-title: Advances in neural networks—ISNN 2006, lecture notes in computer science, vol 3973. year: 2006 ident: 750_CR30 – volume: 265 start-page: 185 year: 2010 ident: 750_CR24 publication-title: J Theor Biol doi: 10.1016/j.jtbi.2010.04.012 – ident: 750_CR31 doi: 10.1016/j.neunet.2007.07.002 – volume: 48 start-page: 153 year: 2010 ident: 750_CR33 publication-title: Artif Intell Med doi: 10.1016/j.artmed.2009.11.001 – volume: 97 start-page: 1 year: 2009 ident: 750_CR3 publication-title: Biosystems doi: 10.1016/j.biosystems.2009.03.006 – volume: 10 start-page: S15 year: 2009 ident: 750_CR34 publication-title: BMC Genom doi: 10.1186/1471-2164-10-S1-S15 – volume: 66 start-page: 31 year: 2002 ident: 750_CR21 publication-title: Biosystems doi: 10.1016/S0303-2647(02)00019-9 – volume: 45 start-page: 346 year: 2010 ident: 750_CR7 publication-title: Adv Appl Math doi: 10.1016/j.aam.2010.03.002 – volume: 223 start-page: 2919 year: 2009 ident: 750_CR18 publication-title: Proc IMechE Part C: J Mech Eng Sci doi: 10.1243/09544062JMES1494 – volume: 91 start-page: 3196 year: 2006 ident: 750_CR29 publication-title: Biophys J doi: 10.1529/biophysj.106.083485 – start-page: 267 volume-title: EKC2008 Proceedings of the EU-Korea conference on science and technology, Springer proceedings in physics, vol 124. year: 2008 ident: 750_CR13 doi: 10.1007/978-3-540-85190-5_28 – ident: 750_CR19 doi: 10.1016/B978-008045157-2/50081-X – volume-title: Probabilistic Boolean networks: the modeling and control of gene regulatory networks year: 2009 ident: 750_CR26 – volume: 5 start-page: e11,793 issue: 8 year: 2010 ident: 750_CR5 publication-title: PLoS ONE doi: 10.1371/journal.pone.0011793 – volume: 4210 start-page: 127 year: 2006 ident: 750_CR28 publication-title: Comput Methods Syst Biol doi: 10.1007/11885191_9 – start-page: 293 volume-title: Unifying themes in complex systems year: 2006 ident: 750_CR9 doi: 10.1007/978-3-540-35866-4_29 – ident: 750_CR15 – volume: 193 start-page: 307 year: 1998 ident: 750_CR17 publication-title: J Theor Biol doi: 10.1006/jtbi.1998.0701 – ident: 750_CR2 doi: 10.1109/INDIN.2009.5195852 – volume: 14 start-page: 94 year: 2003 ident: 750_CR25 publication-title: Genome Inform – volume: 22 start-page: 437 year: 1969 ident: 750_CR10 publication-title: J Theor Biol doi: 10.1016/0022-5193(69)90015-0 – volume: 11 start-page: 59 year: 2010 ident: 750_CR27 publication-title: BMC Bioinform doi: 10.1186/1471-2105-11-59 – start-page: 323 volume-title: Ant colony optimization and swarm intelligence, lecture notes in computer science, vol 5217 year: 2008 ident: 750_CR11 doi: 10.1007/978-3-540-87527-7_33 – volume: 223 start-page: 1051 year: 2009 ident: 750_CR20 publication-title: Proc Inst Mech Eng Part C: J Mech Eng Sci doi: 10.1243/09544062JMES838 – ident: 750_CR22 – volume: 396 start-page: 247 year: 2008 ident: 750_CR6 publication-title: Theor Comput Sci doi: 10.1016/j.tcs.2007.09.008 |
| SSID | ssj0004685 |
| Score | 2.0916402 |
| Snippet | Learning gene regulatory networks under the threshold Boolean network model is presented. To accomplish this, the swarm intelligence technique called the bees... |
| SourceID | unpaywall crossref springer |
| SourceType | Open Access Repository Enrichment Source Index Database Publisher |
| StartPage | 63 |
| SubjectTerms | Artificial Intelligence Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Cont. Dev. of Neural Compt. & Appln Data Mining and Knowledge Discovery Image Processing and Computer Vision Probability and Statistics in Computer Science |
| SummonAdditionalLinks | – databaseName: Unpaywall dbid: UNPAY link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1La9wwEB7SzSH00PSRkpS26FB6aJDjh-y1C6GkjxAKCaFkITmZ0WtjsrWXtZeSPfS3d2zLS0ohpYfeLDGyETPSN3hmvgF4Y8gHUIpOmiYw40JKwzPpGy4szRqtkqwrFD49S04m4utlfLkBQyovujAFGg-NKrRn6jtzBzRq-xXUDSHI-snT1YemmFff3Pjw9OLz20J_LORhFI4JgKIHsJnE5KqPYHNydn501fHviTbjp0_AF1HEye7TIerpdySjBGy8-4FIoMpXv-PWEDR9CFvLco63P3A2u4NLx9vwc6ju6dNRbrxlIz21-pPs8f9s-TE8ch4tO-pN8AlsmPIpbA_dIpi7PJ7Be0flOmVks4bRR9rOYdXilpV9KnrN2iT8KSOXlEljaoazabUomuvvOzA5_nLx6YS7zg1cRVHQcBUmiu4SVKhja0M9TjWiVtpmmYgxkaHSQvo6U7FN00CoOLJWY4K0SgaavNLnMCqr0uwCMymq1AYqMFaKcYhZW9OiY-lnGKUo5R74g15y5WjN2-4as3xNyNypMidV5q0q89UevFsvmfecHvcJ7w_Kzt3xru-VXtvD39_94p-kX8KoWSzNK3KCGvnamfIv4akKZA priority: 102 providerName: Unpaywall |
| Title | Learning gene regulatory networks using the bees algorithm |
| URI | https://link.springer.com/article/10.1007/s00521-011-0750-z http://americanae.aecid.es/americanae/es/registros/registro.do?tipoRegistro=MTD&idBib=3272663 |
| UnpaywallVersion | submittedVersion |
| Volume | 22 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVEBS databaseName: Academic Search Ultimate - eBooks customDbUrl: https://search.ebscohost.com/login.aspx?authtype=ip,shib&custid=s3936755&profile=ehost&defaultdb=asn eissn: 1433-3058 dateEnd: 20241028 omitProxy: true ssIdentifier: ssj0004685 issn: 1433-3058 databaseCode: ABDBF dateStart: 19990101 isFulltext: true titleUrlDefault: https://search.ebscohost.com/direct.asp?db=asn providerName: EBSCOhost – providerCode: PRVEBS databaseName: Inspec with Full Text customDbUrl: eissn: 1433-3058 dateEnd: 20241028 omitProxy: false ssIdentifier: ssj0004685 issn: 1433-3058 databaseCode: ADMLS dateStart: 19930301 isFulltext: true titleUrlDefault: https://www.ebsco.com/products/research-databases/inspec-full-text providerName: EBSCOhost – providerCode: PRVLSH databaseName: SpringerLink Journals customDbUrl: mediaType: online eissn: 1433-3058 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0004685 issn: 1433-3058 databaseCode: AFBBN dateStart: 19970301 isFulltext: true providerName: Library Specific Holdings – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: http://www.proquest.com/pqcentral?accountid=15518 eissn: 1433-3058 dateEnd: 20241028 omitProxy: true ssIdentifier: ssj0004685 issn: 1433-3058 databaseCode: BENPR dateStart: 20120101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVAVX databaseName: SpringerLINK - Czech Republic Consortium customDbUrl: eissn: 1433-3058 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0004685 issn: 1433-3058 databaseCode: AGYKE dateStart: 19970101 isFulltext: true titleUrlDefault: http://link.springer.com providerName: Springer Nature – providerCode: PRVAVX databaseName: SpringerLink Journals (ICM) customDbUrl: eissn: 1433-3058 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0004685 issn: 1433-3058 databaseCode: U2A dateStart: 19970101 isFulltext: true titleUrlDefault: http://www.springerlink.com/journals/ providerName: Springer Nature |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LS8NAEB60PagH32J9sQdPSiDvJt5SbS2KxYOB9hT2WYWYljRF2l_vbl5UkIqHsCTsTmB2Zmd2d2Y-gGsufQBKpaYxacw0mxCu-UTnmi3kV86o6-eJwi8Dtx_aT0NnWOZxz6po9-pKMl-p62Q3dYKptr7ykWZOW25C01HVvKQQh2awkgyZ43DKbYsK6bGt6irzNxI_jVH12x3YmidTvPjCcbxibHr7sFt6iSgopvUANnhyCHsVAgMqFfII7sryqGMk5YCjtACWn6QLlBTh3TOkAtvHSLp5iHA-QzgeT9KP7P3zGMJe9-2-r5VoCBq1LCPTqOlSqZ-YYuYIYbK2xzBmlAnftx3sEpMym-jMp47wPMOmjiUEwy6Wo4jBpKd3Ao1kkvBTQNzD1BMGNbggdtvEvsoTYQ7RfWx5mJAW6BVbIlqWCleIFXFUFznOORlJTkaKk9GyBTf1kGlRJ2Nd59uK11GpMrO1vevp-Jv22b9on8O2meNbKKm4gEaWzvml9DIycgXNoPPQ6an2cfTcvcqlTL6Fg9dg9A0I7s9S |
| linkProvider | Springer Nature |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LT8JAEJ4oHNCD-Iz43IMnTQ1tt6X1RgyI8jhBgqdmn2jEQkqJgV_v9hk0BsOhl2Z32s7OdGZ2Z-YDuBHKB2BMaRpXxkzDlArNpVWhYanuCs5sNy4U7vbs1gC_DK1hWsc9y7LdsyPJ-E-dF7tFO5hR6KsuZea05TYUsYpPjAIU60-v7cZKOWSMxKkClyipB5vZYeZfRH6ao-zBu1Ca-1Oy-CLj8Yq5aZahn71okmXycT8P6T1b_urhuOGX7MNe6n6ieiIvB7Al_EMoZ9AOKNX0I3hI-66OkBIwgYIEsX4SLJCf5I3PUJQxP0LKf0RUiBki49EkeA_fPo9h0Gz0H1taCrOgMdPUQ40ZNlOKTxjhlpQGrzmcEM64dF1sEZsajGNa5S6zpOPomFmmlJzYRM2iOlcu5AkU_IkvTgEJhzBH6kwXkuKaQdyoAIVbtOoS0yGUVqCacdtjaQ_yCApj7OXdk2PeeIo3XsQbb1mB23zKNGnAsW7wXcZxL9XF2drR-Sr_T_tsI9rXUGr1ux2v89xrn8OOEYNoRGt-AYUwmItL5cqE9CoV3W9Qdep_ |
| linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LS8NAEB60go-Db7E-9-BJCeaxSRNvRS31VTxY6C3sswoxLUmKtL_e3byoIBUPewm7Q5idYWZ3Z74P4EKoHIAx5WlcBTMDUyqMgJrCwFJ9FZx5Qd4o_NLzun38OHAHJc9pWlW7V0-SRU-DRmmKs-sxl9d145u-zdTHYDVUyDNmy7CCNU6CMui-3Z5rjMw5OdURRpf3YKd61vxNxM_AVP3CBqxN4jGZfpEomgs8nW3YLDNG1C62eAeWRLwLWxUbAyqdcw9uSqjUIVI2IVBSkMyPkimKi1LvFOki9yFSKR-iQqSIRMNR8pG9f-5Dv3P_dts1SmYEgzmOlRnM9pjyVcIId6W0ecvnhHDGZRBgl3jUZhxTkwfMlb5vYeY6UnLiEbWKWlxlfQfQiEexOAQkfMJ8aTFLSIpbNgl0zwh3qRkQxyeUNsGs1BKyEjZcs1dEYQ14nGsyVJoMtSbDWRMu6yXjAjNj0eSrStdh6T7pwtn1dvwt--hfss9h9fWuEz4_9J6OYd3OaS-0gZxAI0sm4lQlHxk9yw3sG33b0cQ |
| linkToUnpaywall | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1La9wwEB7SzSH00PSRkpS26FB6aJDjh-y1C6GkjxAKCaFkITmZ0WtjsrWXtZeSPfS3d2zLS0ohpYfeLDGyETPSN3hmvgF4Y8gHUIpOmiYw40JKwzPpGy4szRqtkqwrFD49S04m4utlfLkBQyovujAFGg-NKrRn6jtzBzRq-xXUDSHI-snT1YemmFff3Pjw9OLz20J_LORhFI4JgKIHsJnE5KqPYHNydn501fHviTbjp0_AF1HEye7TIerpdySjBGy8-4FIoMpXv-PWEDR9CFvLco63P3A2u4NLx9vwc6ju6dNRbrxlIz21-pPs8f9s-TE8ch4tO-pN8AlsmPIpbA_dIpi7PJ7Be0flOmVks4bRR9rOYdXilpV9KnrN2iT8KSOXlEljaoazabUomuvvOzA5_nLx6YS7zg1cRVHQcBUmiu4SVKhja0M9TjWiVtpmmYgxkaHSQvo6U7FN00CoOLJWY4K0SgaavNLnMCqr0uwCMymq1AYqMFaKcYhZW9OiY-lnGKUo5R74g15y5WjN2-4as3xNyNypMidV5q0q89UevFsvmfecHvcJ7w_Kzt3xru-VXtvD39_94p-kX8KoWSzNK3KCGvnamfIv4akKZA |
| 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=Learning+gene+regulatory+networks+using+the+bees+algorithm&rft.jtitle=Neural+computing+%26+applications&rft.au=Ruz%2C+Gonzalo+A.&rft.au=Goles%2C+Eric&rft.date=2013-01-01&rft.pub=Springer-Verlag&rft.issn=0941-0643&rft.eissn=1433-3058&rft.volume=22&rft.issue=1&rft.spage=63&rft.epage=70&rft_id=info:doi/10.1007%2Fs00521-011-0750-z&rft.externalDocID=10_1007_s00521_011_0750_z |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0941-0643&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0941-0643&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0941-0643&client=summon |