A survey on evolutionary algorithm based hybrid intelligence in bioinformatics

BioMed Research International

Volumn 2014, Issue , 2014, Pages

  Li, Shan ^a   Kang, Liying ^a   Zhao, Xing Ming ^b  

^a SHANGHAI UNIVERSITY   (China)

^b TONGJI UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; BIOINFORMATICS; BIOLOGICAL MODEL; EVOLUTIONARY ALGORITHM; HUMAN; HYBRID INTELLIGENCE; MACHINE LEARNING; MOLECULAR DYNAMICS; PARAMETERS; ALGORITHM; ANIMAL; ARTIFICIAL INTELLIGENCE; BIOLOGY; BIOMIMETICS; EVOLUTION; PROCEDURES;

ALGORITHMS; ANIMALS; ARTIFICIAL INTELLIGENCE; BIOLOGICAL EVOLUTION; BIOMIMETICS; COMPUTATIONAL BIOLOGY; HUMANS; MODELS, GENETIC;

EID: 84897859865     PISSN: 23146133     EISSN: 23146141     Source Type: Journal    
DOI: 10.1155/2014/362738     Document Type: Article

References (58)

1. Barrett T., Edgar R., Gene expression omnibus: microarray data storage, submission, retrieval, and analysis. Methods in Enzymology 2006 411 352 369 2-s2.0-33747884345 10.1016/S0076-6879(06)11019-8 (Pubitemid 44291435)
2. Abecasis G. R., Auton A., Brooks L. D., An integrated map of genetic variation from 1, 092 human genomes. Nature 2012 491 7422 56 65
3. Golub T. R., Slonim D. K., Tamayo P., Huard C., Gaasenbeek M., Mesirov J. P., Coller H., Loh M. L., Downing J. R., Caligiuri M. A., Bloomfield C. D., Lander E. S., Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 1999 286 5439 531 527 2-s2.0-0033569406 10.1126/science.286.5439.531
4. Ramaswamy S., Tamayo P., Rifkin R., Mukherjee S., Yeang C.-H., Angelo M., Ladd C., Reich M., Latulippe E., Mesirov J. P., Poggio T., Gerald W., Loda M., Lander E. S., Golub T. R., Multiclass cancer diagnosis using tumor gene expression signatures. Proceedings of the National Academy of Sciences of the United States of America 2001 98 26 15149 15154 2-s2.0-0347201147 10.1073/pnas.211566398 (Pubitemid 34013984)
5. Liu K. Q., Liu Z. P., Hao J. K., Identifying dysregulated pathways in cancers from pathway interaction networks. BMC Bioinformatics 2012 13, article 126
6. Lanckriet G. R., Deng M., Cristianini N., Jordan M. I., Noble W. S., Kernel-based data fusion and its application to protein function prediction in yeast. Pacific Symposium on Biocomputing 2004 300 311 2-s2.0-2442674422
7. Barh D., Gupta K., Jain N., Conserved host-pathogen PPIs. Globally conserved inter-species bacterial PPIs based conserved host-pathogen interactome derived novel target in C. pseudotuberculosis, C. diphtheriae, M. tuberculosis, C. ulcerans, Y. pestis, and E. coli targeted by Piper betel compounds. Integrative Biology 2013 5 3 495 509
8. Campillos M., Kuhn M., Gavin A.-C., Jensen L. J., Bork P., Drug target identification using side-effect similarity. Science 2008 321 5886 263 266 2-s2.0-47249146126 10.1126/science.1158140 (Pubitemid 351989100)
9. Zhao X.-M., Wang R.-S., Chen L., Aihara K., Uncovering signal transduction networks from high-throughput data by integer linear programming. Nucleic Acids Research 2008 36 9, article e48 2-s2.0-44349093750 10.1093/nar/gkn145 (Pubitemid 351737207)
10. Fraser A. S., Simulation of genetic systems by automatic digital computers. I. Introduction. Australian Journal of Biological Sciences 1957 10 484 491
11. Holland J. H., Adaptation in Natural and Artificial Systems: an Introductory analysis with Applications to Biology, Control, and Artificial Intelligence 1992 MIT Press
12. Barricell N. A., Numerical testing of evolution theories. Journal of Statistical Computation and Simulation 1972 1 2 97 127
13. Rechenberg I., Evolutionsstrategie: Optimierung Technischer Systeme Nach Prinzipien Der Biologischen Evolution 1971 Technical University of Berlin
14. Fogel L. J., Owens A. J., Walsh M. J., Artificial Intelligence Through Simulated Evolution 1966 John Wiley & Sons
15. Storn R., Price K., Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization 1997 11 4 341 359 2-s2.0-0142000477 (Pubitemid 127502202)
16. Li L., Weinberg C. R., Darden T. A., Pedersen L. G., Gene selection for sample classification based on gene expression data: study of sensitivity to choice of parameters of the GA/KNN method. Bioinformatics 2002 17 12 1131 1142 2-s2.0-0036139278 (Pubitemid 34041145)
17. Jirapech-Umpai T., Aitken S., Feature selection and classification for microarray data analysis: evolutionary methods for identifying predictive genes. BMC Bioinformatics 2005 6, article 148 2-s2.0-25444528447 10.1186/1471-2105-6- 148 (Pubitemid 41357999)
18. Goldberg D. E., Holland J. H., Genetic algorithms and machine learning. Machine Learning 1988 3 2 95 99
19. Hong J.-H., Cho S.-B., Efficient huge-scale feature selection with speciated genetic algorithm. Pattern Recognition Letters 2006 27 2 143 150 2-s2.0-27844458593 10.1016/j.patrec.2005.07.009 (Pubitemid 41659800)
20. Chang S. W., Abdul-Kareem S., Merican A. F., Oral cancer prognosis based on clinicopathologic and genomic markers using a hybrid of feature selection and machine learning methods. BMC Bioinformatics 2013 14, article 170
21. Mahdevar G., Zahiri J., Sadeghi M., Nowzari-Dalini A., Ahrabian H., Tag SNP selection via a genetic algorithm. Journal of Biomedical Informatics 2010 43 5 800 804 2-s2.0-77956262207 10.1016/j.jbi.2010.05.011
22. Carlson C. S., Eberle M. A., Rieder M. J., Yi Q., Kruglyak L., Nickerson D. A., Selecting a maximally informative set of single-nucleotide polymorphisms for association analyses using linkage disequilibrium. American Journal of Human Genetics 2004 74 1 106 120 2-s2.0-0346373654 10.1086/381000 (Pubitemid 38085242)
23. Baggerly K. A., Morris J. S., Wang J., Gold D., Xiao L.-C., Coombes K. R., A comprehensive approach to the analysis of matrix-assisted laser desorption/ionization-time of flight proteomics spectra from serum samples. Proteomics 2003 3 9 1667 1672 2-s2.0-0141855279 10.1002/pmic.200300522 (Pubitemid 37129810)
24. Petricoin E. F. III, Ardekani A. M., Hitt B. A., Use of proteomic patterns in serum to identify ovarian cancer. The Lancet 2002 359 572 577 2-s2.0-4143066121 10.1016/S0140-6736(04)16848-7 (Pubitemid 34178440)
25. Li L., Tang H., Wu Z., Gong J., Gruidl M., Zou J., Tockman M., Clark R. A., Data mining techniques for cancer detection using serum proteomic profiling. Artificial Intelligence in Medicine 2004 32 2 71 83 2-s2.0-4444234496 10.1016/j.artmed.2004.03.006 (Pubitemid 39201347)
26. Zhao X.-M., Cheung Y.-M., Huang D.-S., A novel approach to extracting features from motif content and protein composition for protein sequence classification. Neural Networks 2005 18 8 1019 1028 2-s2.0-26944462332 10.1016/j.neunet.2005.07.002 (Pubitemid 41476336)
27. Gong B., Guo Z., Li J., Zhu G., Lv S., Rao S., Li X., Application of a genetic algorithm -Support vector machine hybrid for prediction of clinical phenotypes based on genome-wide SNP profiles of sib pairs. Proceedings of the 2nd International Confernce on Fuzzy Systems and Knowledge Discovery (FSKD '05) August 2005 830 835 2-s2.0-26944469029
28. Li L., Jiang W., Li X., Moser K. L., Guo Z., Du L., Wang Q., Topol E. J., Wang Q., Rao S., A robust hybrid between genetic algorithm and support vector machine for extracting an optimal feature gene subset. Genomics 2005 85 1 16 23 2-s2.0-20844445085 10.1016/j.ygeno.2004.09.007 (Pubitemid 39655687)
29. Huang W.-L., Tung C.-W., Huang H.-L., Hwang S.-F., Ho S.-Y., ProLoc: prediction of protein subnuclear localization using SVM with automatic selection from physicochemical composition features. BioSystems 2007 90 2 573 581 2-s2.0-34548458988 10.1016/j.biosystems.2007.01.001 (Pubitemid 47362472)
30. Fernández M., Miranda-Saavedra D., Genome-wide enhancer prediction from epigenetic signatures using genetic algorithm-optimized support vector machines. Nucleic Acids Research 2012 40 10 e77 10.1093/nar/gks149
31. Saeys Y., Degroeve S., Aeyels D., Rouzé P., Van de Peer Y., Feature selection for splice site prediction: a new method using EDA-based feature ranking. BMC Bioinformatics 2004 5, article 64 2-s2.0-2942541354 10.1186/1471-2105-5-64 (Pubitemid 38751272)
32. Nemati S., Basiri M. E., Ghasem-Aghaee N., Aghdam M. H., A novel ACO-GA hybrid algorithm for feature selection in protein function prediction. Expert Systems with Applications 2009 36 10 12086 12094 2-s2.0-69149109819 10.1016/j.eswa.2009.04.023
33. Kamath U., Compton J., Islamaj-Dogan R., De Jong K. A., Shehu A., An evolutionary algorithm approach for feature generation from sequence data and its application to DNA splice site prediction. IEEE/ACM Transactions on Computational Biology and Bioinformatics 2012 9 5 1387 1398
34. Zhan C., Yeung L. F., Parameter estimation in systems biology models using spline approximation. BMC Systems Biology 2011 5, article 14 2-s2.0-78751647229 10.1186/1752-0509-5-14
35. Ashyraliyev M., Fomekong-Nanfack Y., Kaandorp J. A., Blom J. G., Systems biology: parameter estimation for biochemical models. FEBS Journal 2009 276 4 886 902 2-s2.0-58849106769 10.1111/j.1742-4658.2008.06844.x
36. Banga J. R., Balsa-Canto E., Parameter estimation and optimal experimental design. Essays in Biochemistry 2008 45 195 209 2-s2.0-58149232578 10.1042/BSE0450195
37. Moles C. G., Mendes P., Banga J. R., Parameter estimation in biochemical pathways: a comparison of global optimization methods. Genome Research 2003 13 11 2467 2474 2-s2.0-0242574982 10.1101/gr.1262503 (Pubitemid 37428271)
38. Katsuragi T., Ono N., Yasumoto K., SS-mPMG and SS-GA: tools for finding pathways and dynamic simulation of metabolic networks. Plant Cell Physiology 2013 54 5 728 739 10.1093/pcp/pct052
39. Ueda T., Tominaga D., Araki N., Estimate hidden dynamic profiles of siRNA effect on apoptosis. BMC Bioinformatics 2013 14, article 97
40. Abdullah A., Deris S., Anwar S., Arjunan S. N., An evolutionary firefly algorithm for the estimation of nonlinear biological model parameters. PloS One 2013 8 3 e56310
41. Savageau M. A., Rosen R., Biochemical Systems Analysis: A Study of Function and Design in molecular Biology (1976 Addison-Wesley
42. Tominaga D., Okamoto M., Design of canonical model describing complex nonlinear dynamics. Proceedings of the 7th International Conference on Computer Applications in Biotechnology 1998 85 90
43. Kikuchi S., Tominaga D., Arita M., Takahashi K., Tomita M., Dynamics modeling of genetic networks using genetic algorithm and S-system. Bioinformatics 2003 19 5 643 650 2-s2.0-0037461033 10.1093/bioinformatics/btg027 (Pubitemid 36417059)
44. Tsutsui S., Yamamura M., Higuchi T., Multi-parent recombination with simplex crossover in real coded genetic algorithms. Proceedings of the Genetic and Evolutionary Computation Conference 1999 657 664
45. Noman N., Iba H., Reverse engineering genetic networks using evolutionary computation. Genome Informatics 2005 16 2 205 214 2-s2.0-33748530630
46. Moscato P., On evolution, search, optimization, genetic algorithms and martial arts: towards memetic algorithms. Caltech Concurrent Computation Program 1989 826
47. Chowdhury A. R., Chetty M., Vinh N. X., Incorporating time-delays in S-System model for reverse engineering genetic networks. BMC Bioinformatics 2013 14, article 196 10.1186/1471-2105-14-196
48. Repsilber D., Liljenström H., Andersson S. G. E., Reverse engineering of regulatory networks: simulation studies on a genetic algorithm approach for ranking hypotheses. BioSystems 2002 66 1-2 31 41 2-s2.0-0036627676 10.1016/S0303-2647(02)00019-9 (Pubitemid 36169922)
49. Mendoza M. R., Bazzan A. L. C., Evolving random boolean networks with genetic algorithms for regulatory networks reconstruction. Proceedings of the 13th Annual Genetic and Evolutionary Computation Conference (GECCO '11) July 2011 291 298 2-s2.0-84860415026 10.1145/2001576.2001617
50. Esmaeili A., Jacob C., A multi-objective differential evolutionary approach toward more stable gene regulatory networks. BioSystems 2009 98 3 127 136 2-s2.0-71749121782 10.1016/j.biosystems.2009.09.002
51. Terfve C., Cokelaer T., Henriques D., CellNOptR: a flexible toolkit to train protein signaling networks to data using multiple logic formalisms. BMC Systems Biology 2012 6, article 133
52. Crespo I., Krishna A., Le Bechec A., del Sol A., Predicting missing expression values in gene regulatory networks using a discrete logic modeling optimization guided by network stable states. Nucleic Acids Research 2013 41 1, article e8 10.1093/nar/gks785
53. Yu J., Smith V. A., Wang P. P., Hartemink A. J., Jarvis E. D., Advances to Bayesian network inference for generating causal networks from observational biological data. Bioinformatics 2004 20 18 3594 3603 2-s2.0-12344259602 10.1093/bioinformatics/bth448 (Pubitemid 40136795)
54. Xing Z., Wu D., Modeling multiple time units delayed gene regulatory network using dynamic Bayesian network. Proceedings of the 6th IEEE International Conference on Data Mining-Workshops (ICDM '06) December 2006 190 195 2-s2.0-78449303632
55. Li Y., Ngom A., The max-min high-order dynamic Bayesian network learning for identifying gene regulatory networks from time-series microarray data. Proceedings of the IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB '13) 2013 83 90
56. Rozenberg G., Engelfriet E., Elementary net systems. Lectures on Petri Nets I: Basic Models 1998 1497 12 121
57. Nummela J., Juistrom B. A., Evolving, petri nets to represent metabolic pathways. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '05) June 2005 2133 2139 2-s2.0-32444451211 10.1145/1068009.1068361
58. Koh G., Teong H. F. C., Clément M.-V., Hsu D., Thiagarajan P. S., A decompositional approach to parameter estimation in pathway modeling: a case study of the Akt and MAPK pathways and their crosstalk. Bioinformatics 2006 22 14 e271 e280 2-s2.0-33747876155 10.1093/bioinformatics/btl264 (Pubitemid 44288296)