Skip to content
2000
  1. Home
  2. A-Z Publications
  3. Current Alzheimer Research
  4. Volume 21, Issue 5
  5. Article
Volume 21, Issue 5
  • ISSN: 1567-2050
  • E-ISSN: 1875-5828

Abstract

Introduction

When Ant Colony Optimization algorithm (ACO) is adept at identifying the shortest path, the temporary solution is uncertain during the iterative process. All temporary solutions form a solution set.

Methods

Where each solution is random. That is, the solution set has entropy. When the solution tends to be stable, the entropy also converges to a fixed value. Therefore, it was proposed in this paper that apply entropy as a convergence criterion of ACO. The advantage of the proposed criterion is that it approximates the optimal convergence time of the algorithm.

Results

In order to prove the superiority of the entropy convergence criterion, it was used to cluster gene chip data, which were sampled from patients of Alzheimer’s Disease (AD). The clustering algorithm is compared with six typical clustering algorithms. The comparison shows that the ACO using entropy as a convergence criterion is of good quality.

Conclusion

At the same time, applying the presented algorithm, we analyzed the clustering characteristics of genes related to energy metabolism and found that as AD occurs, the entropy of the energy metabolism system decreases; that is, the system disorder decreases significantly.

© 2024 Bentham Science Publishers
Loading

Article metrics loading...

/content/journals/car/10.2174/0115672050325388240823092338
2024-09-13
2025-06-26

  • From This Site

    /content/journals/car/10.2174/0115672050325388240823092338
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. GambardellaL. DorigoM. Solving symmetric and asymmetric TSPs by ant colonies.Proceedings of the Proceedings of IEEE International Conference on Evolutionary Computation199662262710.1109/ICEC.1996.542672
     [Google Scholar]
  2. DorigoM. GambardellaL.M. Ant colony system: A cooperative learning approach to the traveling salesman problem.IEEE Trans. Evol. Comput.199711536610.1109/4235.585892
     [Google Scholar]
  3. DorigoM. StützleT. The ant colony optimization metaheuristic: Algorithms, applications, and advances.Handbook of Metaheuristics GloverF. KochenbergerG.A. Boston, MASpringer US200325028510.1007/0‑306‑48056‑5_9
     [Google Scholar]
  4. DorigoM. CaroG.D. GambardellaL.M. Ant algorithms for discrete optimization.Artif. Life19995213717210.1162/10645469956872810633574
     [Google Scholar]
  5. ShapiroJ. Genetic algorithms in machine learning.Machine Learning and Its ApplicationsSpringer199914616810.1007/3‑540‑44673‑7_7.
     [Google Scholar]
  6. BallM. MagnantiT. MonmaC. NemhauserG. Handbooks in operation research and management science.Elsevier Science19958
     [Google Scholar]
  7. KirkpatrickS. GelattC.D.Jr VecchiM.P. Optimization by simulated annealing.Science1983220459867168010.1126/science.220.4598.67117813860
     [Google Scholar]
  8. GloverF. MulveyJ.M. HoylandK. Solving dynamic stochastic control problems in finance using tabu search with variable scaling.Meta-Heuristics: Theory and Applications. OsmanI.H. KellyJ.P. Boston, MASpringer US199642944810.1007/978‑1‑4613‑1361‑8_26
     [Google Scholar]
  9. GoldbergD.E. HollandJ.H. Genetic algorithms and machine learning.Machine Learning1989959910.1023/A:1022602019183.
     [Google Scholar]
  10. GambardellaL.M. DorigoM. Ant-Q: A reinforcement learning approach to the traveling salesman problem.Machine learning proceedings 1995199525226010.1016/B978‑1‑55860‑377‑6.50039‑6
     [Google Scholar]
  11. GambardellaL.M. TaillardÉ.D. DorigoM. Ant colonies for the quadratic assignment problem.J. Oper. Res. Soc.199950216717610.1057/palgrave.jors.2600676
     [Google Scholar]
  12. BullnheimerB. HartlR.F. StraussC. Applying the ANT system to the vehicle routing problem. 2nd International Conference on Metaheuristics - MIC97Sophia-Antipolis, France199710.1007/978‑1‑4615‑5775‑3_20
     [Google Scholar]
  13. ParpinelliR.S. LopesH.S. FreitasA.A. Data mining with an ant colony optimization algorithm.IEEE Trans. Evol. Comput.20026432133210.1109/TEVC.2002.802452
     [Google Scholar]
  14. GutjahrW.J. ACO algorithms with guaranteed convergence to the optimal solution.Inf. Process. Lett.200282314515310.1016/S0020‑0190(01)00258‑7
     [Google Scholar]
  15. DuanH. Ant colony optimization: Principle, convergence and application.Handbook of Swarm Intelligence: Concepts, Principles and Applications.Springer201137338810.1007/978‑3‑642‑17390‑5_16
     [Google Scholar]
  16. YooJ.H. LaR.J. MakowskiA.M. Convergence results for ant routing.Proceedings of the proceedings of the conference on information sciences and systems2004
     [Google Scholar]
  17. YooJ.H. LaR.J. MakowskiA.M. Convergence of ant routing algorithms–Results for a simple parallel network and perspectives.Technical Report CSHCN 2003-44, University of Maryland2003
     [Google Scholar]
  18. SunT. WangX.K. LiuY.X. ZhangM.j. Ant algorithm and analysis on its convergence.Minimicro Systems-Shenyang20032415241527
     [Google Scholar]
  19. DingJ.L. ChenZ.Q. YuanZ.Z. On the Markov convergence analysis for the combination of genetic algorithm and ant algorithm.Acta Automatica Sinica200430629634
     [Google Scholar]
  20. HouY.H. WuY.W. LuL.J. XiongX.Y. Generalized ant colony optimization for economic dispatch of power systems.Proceedings international conference on power system technologyKunming, China200222522910.1109/ICPST.2002.1053539
     [Google Scholar]
  21. ShannonC.E. A mathematical theory of communication.Bell Syst. Tech. J.194827337942310.1002/j.1538‑7305.1948.tb01338.x
     [Google Scholar]
  22. GuoW. SunY. PangX. YangL. YuL. ZhangQ. YangP. PanJ.S. PangC. A novel crossover operator based on grey wolf optimizer applied to feature selection problem.Genetic and Evolutionary Computing20249810710.1007/978‑981‑99‑9412‑0_11.
     [Google Scholar]
  23. SunX. ShuW. ZhangY. HuangX. LiuJ. LiuY. YangT. Identification of Alzheimer’s disease associated genes through explicable deep learning and bioinformatic.Proceedings of the 2023 IEEE 4th international conference on pattern recognition and machine learning (PRML)200332032710.1109/PRML59573.2023.10348276
     [Google Scholar]
  24. Chelly DZ. AvdeyevP. BayzidM.S. Biological computation and computational biology: survey, challenges, and discussion.Artif. Intell. Rev.20215464169423510.1007/s10462‑020‑09951‑1
     [Google Scholar]
  25. ZhangY. KiryuH. Identification of oxidative stress-related genes differentially expressed in Alzheimer’s disease and construction of a hub gene-based diagnostic model.Sci. Rep.2023131681710.1038/s41598‑023‑34021‑137100862
     [Google Scholar]
  26. StützleT. DorigoM. A short convergence proof for a class of ant colony optimization algorithms.IEEE Trans. Evol. Comput.20026435836510.1109/TEVC.2002.802444
     [Google Scholar]
  27. ColorniA. DorigoM. ManiezzoV. Distributed optimization by ant colonies.Proceedings of the Proceedings of the first European conference on artificial lifeParis, France1991142134142
     [Google Scholar]
  28. PangC.Y. Vector quantization and image compression.Ph. D. thesis, Department of Computer Science2002
     [Google Scholar]
  29. LiuT. PorterJ. ZhaoC. ZhuH. WangN. SunZ. MoY.Y. WangZ. TADKB: Family classification and a knowledge base of topologically associating domains.BMC Genomics201920121710.1186/s12864‑019‑5551‑230871473
     [Google Scholar]
  30. Ferrer-FontL. MayerJ.U. OldS. HermansI.F. IrishJ. PriceK.M. High-dimensional data analysis algorithms yield comparable results for mass cytometry and spectral flow cytometry data.Cytometry A202097882483110.1002/cyto.a.2401632293794
     [Google Scholar]
  31. HartiganJ.A. Clustering algorithmsJohn Wiley & Sons, Inc.1975
     [Google Scholar]
  32. EisenM.B. SpellmanP.T. BrownP.O. BotsteinD. Cluster analysis and display of genome-wide expression patterns.Proc. Natl. Acad. Sci. USA19989525148631486810.1073/pnas.95.25.148639843981
     [Google Scholar]
  33. SaldanhaA.J. Java Treeview—extensible visualization of microarray data.Bioinformatics200420173246324810.1093/bioinformatics/bth34915180930
     [Google Scholar]
  34. KaufmanL. RousseeuwP.J. Finding groups in data: An introduction to cluster analysis.John Wiley199010.2307/2532178.
     [Google Scholar]
  35. PirimH. EkşioğluB. PerkinsA.D. YüceerÇ. Clustering of high throughput gene expression data.Comput. Oper. Res.201239123046306110.1016/j.cor.2012.03.00823144527
     [Google Scholar]
  36. ChengH. YangS. CaoJ. Dynamic genetic algorithms for the dynamic load balanced clustering problem in mobile ad hoc networks.Expert Syst. Appl.20134041381139210.1016/j.eswa.2012.08.050
     [Google Scholar]
  37. DhillonI.S. Co-clustering documents and words using bipartite spectral graph partitioning.200110.1145/502512.502550
     [Google Scholar]
  38. CiaramellaA. NardoneD. StaianoA. Data integration by fuzzy similarity-based hierarchical clustering.BMC Bioinformatics202021S1035010.1186/s12859‑020‑03567‑632838739
     [Google Scholar]
  39. ChatterjeeS. DasA. An ensemble algorithm integrating consensus-clustering with feature weighting based ranking and probabilistic fuzzy logic-multilayer perceptron classifier for diagnosis and staging of breast cancer using heterogeneous datasets.Appl. Intell.20235311138821392310.1007/s10489‑022‑04157‑0
     [Google Scholar]
  40. ChangH. YeungD.Y. Robust path-based spectral clustering.Pattern Recognit.200841119120310.1016/j.patcog.2007.04.010
     [Google Scholar]
  41. LiZ. NieF. ChangX. YangY. ZhangC. SebeN. Dynamic affinity graph construction for spectral clustering using multiple features.IEEE Trans. Neural Netw. Learn. Syst.201829126323633210.1109/TNNLS.2018.282986729994548
     [Google Scholar]
  42. LvY. ZhuX. ZhuZ. QuA. Nonparametric cluster analysis on multiple outcomes of longitudinal data.Stat. Sin.2020301829185610.5705/ss.202018.0032
     [Google Scholar]
  43. RenY. WangN. LiM. XuZ. Deep density-based image clustering.Knowl. Base. Syst.202019710584110.1016/j.knosys.2020.105841
     [Google Scholar]
  44. WoodmanR.J. MangoniA.A. A comprehensive review of machine learning algorithms and their application in geriatric medicine: Present and future.Aging Clin. Exp. Res.202335112363239710.1007/s40520‑023‑02552‑237682491
     [Google Scholar]
  45. GuoY.B. ZhengZ.X. KongL.J. GuoW. YanZ.M. CuiL.Z. Xiao-Fang WangA. A novel multi-view bi-clustering method for identifying abnormal co-occurrence medical visit behaviors.Methods2022207657310.1016/j.ymeth.2022.09.00436122881
     [Google Scholar]
  46. LiH. HerfortB. HuangW. ZiaM. ZipfA. Exploration of OpenStreetMap missing built-up areas using twitter hierarchical clustering and deep learning in Mozambique.ISPRS J. Photogramm. Remote Sens.2020166415110.1016/j.isprsjprs.2020.05.007
     [Google Scholar]
  47. WuX. ShiZ. ZouZ. A geographic information-driven method and a new large scale dataset for remote sensing cloud/snow detection.ISPRS J. Photogramm. Remote Sens.20211748710410.1016/j.isprsjprs.2021.01.023
     [Google Scholar]
  48. MillerB.F. Bambah-MukkuD. DulacC. ZhuangX. FanJ. Characterizing spatial gene expression heterogeneity in spatially resolved single-cell transcriptomic data with nonuniform cellular densities.Genome Res.202131101843185510.1101/gr.271288.12034035045
     [Google Scholar]
  49. ShengW. TuckerA. LiuX. A niching genetic k-means algorithm and its applications to gene expression data.Soft Comput.201014191910.1007/s00500‑008‑0386‑9
     [Google Scholar]
  50. Abdel-MaksoudE. ElmogyM. Al-AwadiR. Brain tumor segmentation based on a hybrid clustering technique.Egyptian Inform J2015161718110.1016/j.eij.2015.01.003
     [Google Scholar]
  51. KarimM.R. BeyanO. ZappaA. CostaI.G. Rebholz-SchuhmannD. CochezM. DeckerS. Deep learning-based clustering approaches for bioinformatics.Brief. Bioinform.202122139341510.1093/bib/bbz17032008043
     [Google Scholar]
  52. TangT. ChenS. ZhaoM. HuangW. LuoJ. Very large-scale data classification based on K-means clustering and multi-kernel SVM.Soft Comput.201923113793380110.1007/s00500‑018‑3041‑0
     [Google Scholar]
  53. YanD. HuangL. JordanM.I. Fast approximate spectral clustering.Proceedings of the proceedings of the 15th ACM SIGKDD international conference on knowledge discovery and data miningParis, France200910.1145/1557019.1557118.
     [Google Scholar]
  54. ZhangG.Y. ZhouY.R. WangC.D. HuangD. HeX.Y. Joint representation learning for multi-view subspace clustering.Expert Syst. Appl.202116611391310.1016/j.eswa.2020.113913
     [Google Scholar]
  55. ZhangQ. ChenB. YangP. WuJ. PangX. PangC. Bioinformatics-based study reveals that AP2M1 is regulated by the circRNA-miRNA-mRNA interaction network and affects Alzheimer’s disease.Front. Genet.202213104978610.3389/fgene.2022.104978636468008
     [Google Scholar]
  56. ZhangQ. YangP. PangX. GuoW. SunY. WeiY. PangC. Preliminary exploration of the co-regulation of Alzheimer’s disease pathogenic genes by microRNAs and transcription factors.Front. Aging Neurosci.202214106960610.3389/fnagi.2022.106960636561136
     [Google Scholar]
  57. XiongJ. PangX. SongX. YangL. PangC. The coherence between PSMC6 and α-ring in the 26S proteasome is associated with Alzheimer’s disease.Front. Mol. Neurosci.202416133085310.3389/fnmol.2023.133085338357597
     [Google Scholar]
  58. YangL. PangX. GuoW. ZhuC. YuL. SongX. WangK. PangC. An exploration of the coherent effects between METTL3 and NDUFA10 on Alzheimer’s disease.Int. J. Mol. Sci.202324121011110.3390/ijms24121011137373264
     [Google Scholar]
  59. YangX. GuoW. YangL. LiX. ZhangZ. PangX. LiuJ. PangC. The relationship between protein modified folding molecular network and Alzheimer’s disease pathogenesis based on BAG2-HSC70-STUB1-MAPT expression patterns analysis.Front. Aging Neurosci.202315109040010.3389/fnagi.2023.109040037251806
     [Google Scholar]
  60. LiuZ. ChaillouT. Santos AlvesE. MaderT. JudeB. FerreiraD.M.S. HynynenH. ChengA.J. JonssonW.O. PirontiG. AnderssonD.C. KenneE. RuasJ.L. TaviP. LannerJ.T. Mitochondrial NDUFA4L2 is a novel regulator of skeletal muscle mass and force.FASEB J.20213512e2201010.1096/fj.202100066R34724256
     [Google Scholar]
  61. HouT. ZhangR. JianC. DingW. WangY. LingS. MaQ. HuX. ChengH. WangX. NDUFAB1 confers cardio-protection by enhancing mitochondrial bioenergetics through coordination of respiratory complex and supercomplex assembly.Cell Res.201929975476610.1038/s41422‑019‑0208‑x31366990
     [Google Scholar]
  62. HuttulaS. VäyrynenH. HelisalmiS. KytövuoriL. LuukkainenL. HiltunenM. RemesA.M. KrügerJ. NDUFA1 p.Gly32Arg variant in early-onset dementia.Neurobiol. Aging202211411311610.1016/j.neurobiolaging.2021.09.02635131137
     [Google Scholar]
  63. PotluriP. DavilaA. Ruiz-PesiniE. MishmarD. O’HearnS. HancockS. SimonM. SchefflerI.E. WallaceD.C. ProcaccioV. A novel NDUFA1 mutation leads to a progressive mitochondrial complex I-specific neurodegenerative disease.Mol. Genet. Metab.200996418919510.1016/j.ymgme.2008.12.00419185523
     [Google Scholar]
  64. PanJ.S. ZhangL.G. WangR.B. SnášelV. ChuS.C. Gannet optimization algorithm: A 775 new metaheuristic algorithm for solving engineering optimization problems. Mathematics and 776.Jisuanji Fangzhen2022202343373
     [Google Scholar]
  65. SongP.C. ChuS.C. PanJ.S. YangH. Simplified Phasmatodea population evolution algorithm for optimization.Complex & Intelligent Systems2022842749276710.1007/s40747‑021‑00402‑0
     [Google Scholar]
  66. YuL. TanX. LuoD. YangL. PangX. ShanZ. ZhuC. PanJ.S. PangC. Chebyshev inequality and the identification of genes associated with Alzheimer’s disease.Genetic and Evolutionary ComputingSpringer2024879710.1007/978‑981‑99‑9412‑0_10.
     [Google Scholar]
/content/journals/car/10.2174/0115672050325388240823092338
Loading
Using Entropy as the Convergence Criteria of Ant Colony Optimization and the Application at Gene Chip Data Analysis
Curr Alzheimer Res 21, 324 (2024); https://doi.org/10.2174/0115672050325388240823092338
/content/journals/car/10.2174/0115672050325388240823092338
/content/journals/car/10.2174/0115672050325388240823092338
Loading

Data & Media loading...


  • Article Type:     Research Article
Keyword(s): Alzheimer’s disease; Ant colony algorithms; energy metabolism system; entropy; entropy convergence criterion; gene chip data analysis

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test