Natural computing methods in bioinformatics: A survey

Information Fusion

Volumn 10, Issue 3, 2009, Pages 211-216

  Masulli, Francesco ^a,b   Mitra, Sushmita ^c  

^a UNIVERSITY OF GENOA   (Italy)

^b TEMPLE UNIVERSITY   (United States)

^c INDIAN STATISTICAL INSTITUTE   (India)

Author keywords

Bioinformatics; Biological networks modeling; Microarray data analysis; Natural computing; Protein structure prediction

Indexed keywords

FUZZY NEURAL NETWORKS; FUZZY SETS; LEARNING ALGORITHMS; SURVEYS;

BIOLOGICAL DATUM; BIOLOGICAL NETWORKS MODELING; COMPLEX PROBLEMS; DATA ANALYSIS; GENE REGULATORY NETWORKS; LEARNING ABILITIES; MICROARRAY DATA ANALYSIS; MULTI SENSORS; MULTI SOURCES; NATURAL COMPUTING; PROTEIN STRUCTURE PREDICTION; ROUGH SETS; SEARCH SPACES; UNCERTAINTY HANDLINGS;

BIOINFORMATICS;

EID: 61749086580     PISSN: 15662535     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.inffus.2008.12.002     Document Type: Article

References (73)

1. Collins F.S., and McKusick V.A. Implications of the human genome project for medical science. The Journal of the American Medical Association 285 (2001) 540-544
2. Watson J.D. The human genome project: past, present, and future. Science 248 (1990) 44-49
3. Zvelebil M., and Baum J. Understanding Bioinformatics (2007), Garland Science
4. Mount D.W. Bioinformatics: Sequence and Genome Analysis (2004), Cold Spring Harbor Laboratory Press
5. Baldi P., and Brunak S. Bioinformatics: The Machine Learning Approach. second ed. (2001), The MIT Press
6. Haykin S. Neural Networks. A Comprehensive Foundation. second ed. (1999), Prentice-Hall, Upper Saddle River, NJ
7. Lin C.-T., and Lee C.S.G. Neural Fuzzy Systems: A Neuro-Fuzzy Synergism to Intelligent Systems (1996), Prentice-Hall, Upper Saddle River, NJ
8. Bishop C.M. Pattern Recognition and Machine Learning (2007), Springer
9. Michalewicz Z. Genetic Algorithms + Data Structures = Evolution Programs (1998), Springer
10. Baeck T. Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithm (1996), Oxford University Press
11. Eberhart R.C., Kennedy J., and Shi Y. Swarm Intelligence (2001), Elsevier Science
12. Ishida Y. Immunity-Based Systems (2004), Springer
13. Kirkpatrick S., Gelatt C.D., and Vecchi M.P. Optimization by simulated annealing. Science 220 (1983) 661-680
14. Mitra S., Datta S., Perkins T., and Michailidis G. Introduction to Machine Learning and Bioinformatics (2008), Chapman & Hall/CRC Press
15. Berman H., et al. The protein data bank. Nucleic Acids Research 28 (2000) 235-242
16. Berg J., Tymoczko J., Stryer L., and Clarke N. Biochemistry. fifth ed. (2002), W.H. Freeman & Co., San Francisco, CA
17. Moult J., et al. Critical assessment of methods of protein structure prediction-Round VII. Proteins 69 (2007) 3-9
18. Zhang Y. Progress and challenges in protein structure prediction. Current Opinion in Structural Biology 18 (2008) 342-348
19. Baker D., and Sali A. Protein structure prediction and structural genomics. Science 294 (2001) 93-96
20. Qian N., and Sejnowski T.J. Predicting the secondary structure of globular proteins using neural network models. Journal of Molecular Biology 202 (1988) 865-884
21. Bohr H., et al. Protein secondary structure and homology by neural networks: the a-helices in rhodopsin. FEBS Letters 241 (1988) 223
22. Holley L.H., and Karplus M. Protein secondary structure prediction with a neural network. PNAS 86 (1989) 152-156
23. Kneller D.G., Cohen F.E., and Langridge R. Improvements in protein secondary structure prediction by an enhanced neural network. Journal of Molecular Biology 214 1 (1990) 171-182
24. Stolorz P., Lapedes A., and Xia Y. Predicting protein secondary structure using neural net and statistical methods. Journal of Molecular Biology 225 (1992) 363-377
25. Sasagawa F., and Tajima K. Prediction of protein secondary structures by a neural network. Computer Applications in the Biosciences 9 (1993) 147-152
26. Fariselli P., Compiani M., and Casadio R. Predicting secondary structures of membrane proteins with neural networks. European Biophysics Journal 22 (1993) 41-51
27. Rost B., and Sander C. Improved prediction of protein secondary structure by use of sequence profiles and neural networks. Proceedings of the National Academy of Sciences of the United States of America 90 (1993) 7558-7562
28. Riis S.K., and Krogh A. Improving prediction of protein secondary structure using structured neural networks and multiple sequence alignments. Journal of Computational Biology 3 (1996) 163-183
29. Jones D. Protein secondary structure prediction based on position- specific scoring matrices. Journal of Molecular Biology 292 (1999) 195-202
30. Karplus K., Barrett C., and Hughey R. Hidden Markov models for detecting remote protein homologies. Bioinformatics 14 (1998) 846-856
31. Baldi P., et al. Exploiting the past and the future in protein secondary structure prediction. Bioinformatics 15 (1999) 937-946
32. Pollastri G., and McLysaght A. Porter: a new accurate server for protein secondary structure prediction. Bioinformatics 21 (2005) 1719-1720
33. Pollastri G., Przybylski D., Rost B., and Baldi P. Improving the prediction of protein secondary structure in three and eight classes using recurrent neural networks and profiles. Proteins 47 (2002) 228-235
34. Ceroni A., Frasconi P., and Pollastri G. Learning protein secondary structure from sequential and relational data. Neural Networks 18 (2005) 1029-1039
35. Pedersen J.T., and Moult J. Protein folding simulations with genetic algorithms and a detailed molecular description. Journal of Molecular Biology 269 (1997) 240-259
36. Cutello V., Narzisi G., and Nicosia G. A multi-objective evolutionary approach to the protein structure prediction problem. Journal of The Royal Society Interface 3 (2006) 139-151
37. Blanco A., Pelta D.A., and Verdegay J.-L. Applying a fuzzy sets-based heuristic to the protein structure prediction problem. International Journal of Intelligent Systems 17 (2002) 629-643
38. Pelta D., et al. A fuzzy sets based generalization of contact maps for the overlap of protein structures. Journal of Fuzzy Sets and Systems 152 (2005) 103-123
39. Cheng J., and Baldi P. A machine learning information retrieval approach to protein fold recognition. Bioinformatics 22 (2006) 1456-1463
40. Bohr H., et al. A novel approach to prediction of the 3-dimensional structures of protein backbones by neural networks. FEBS Letters 261 (1990) 43-46
41. Milik M., Kolinski A., and Skolnick J. Neural network system for the evaluation of side-chain packing in protein structures. Protein Engineering 8 (1995) 225-236
42. Simons K.T., Kooperberg C., Huang E., and Baker D. Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and Bayesian scoring functions. Journal of Molecular Biology 268 (1997) 209-225
43. Meiler J., and Baker D. Coupled prediction of protein secondary and tertiary structure. Proceedings of the National Academy of Sciences of the United States of America 100 (2003) 12105-12110
44. Yeung K.Y., Haynor D.R., and Ruzzo W.L. Validating clustering for gene expression data. Bioinformatics 17 (2001) 309-318
45. Bezdek J.C. Pattern Recognition with Fuzzy Objective Function Algorithms (1981), Plenum Press, New York
46. Dembele D., and Kastner P. Fuzzy c-means method for clustering microarray data. Bioinformatics 19 (2003) 973-980
47. Gasch A.P., and Eisen M.B. Exploring the conditional coregulation of yeast gene expression through fuzzy k-means clustering. Genome Biology 3 (2002) 1-22
48. Carpenter G.A., Grossberg S., and Rosen D.B. Fuzzy ART: fast stable learning and categorization of analog patterns by an adaptive resonance system. Neural Networks 4 (1991) 759-771
49. Tomida S., Hanai T., Honda H., and Kobayashi T. Analysis of expression profile using fuzzy adaptive resonance theory. Bioinformatics 18 (2002) 1073-1083
50. S. Bleuler, A. Prelić, E. Zitzler, An EA framework for biclustering of gene expression data, in: Proceedings of Congress on Evolutionary Computation, 2004, pp. 166-173.
51. Ogata H., Goto S., Sato K., Fujibuchi W., Bono H., and Kanehisa M. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Research 27 (1999) 29-34
52. De Jong H. Modeling and simulation of genetic regulatory systems: a literature review. Journal of Computational Biology 9 (2002) 67-103
53. D'haeseleer P., Liang S., and Somogyi R. Genetic network inference: from co-expression clustering to reverse engineering. Bioinformatics 16 (2000) 707-726
54. Thieffry D., Huerta A.M., Pérez-Rueda E., and Collado-Vides J. From specific gene regulation to genomic networks: a global analysis of transcriptional regulation in Escherichia coli. BioEssays 20 (1998) 433-440
55. Vohradsky J. Neural network model of gene expression. FASEB Journal 15 (2001) 846-854
56. Ressom H., Wang D., and Natarajan P. Clustering gene expression data using adaptive double self-organizing map. Physiological Genomics 14 (2003) 35-46
57. Woolf P.J., and Wang Y. A fuzzy logic approach to analyzing gene expression data. Physiological Genomics 3 (2000) 9-15
58. Ressom H., Reynolds R., and Varghese R.S. Increasing the efficiency of fuzzy logic-based gene expression data analysis. Physiological Genomics 13 (2003) 107-117
59. Sokhansanj B.A., Fitch J.P., Quong J.N., and Quong A.A. Linear fuzzy gene network models obtained from microarray data by exhaustive search. BMC Bioinformatics 5 (2004) 108
60. Iba H., and Mimura A. Inference of a gene regulatory network by means of interactive evolutionary computing. Information Science 145 (2002) 225-236
61. D.C. Weaver, C.T. Workman, G.D. Stormo, Modelling regulatory networks with weight matrices, in: Proceedings of Pacific Symposium on Biocomputing, 1999, pp. 112-123.
62. Keedwell E., and Narayanan A. Discovering gene networks with a neural-genetic hybrid. IEEE Transactions on Computational Biology and Bioinformatics 2 (2005) 231-242
63. Mitra S., Das R., Banka H., and Mukhopadhyay S. Gene interaction - an evolutionary biclustering approach. Information Fusion 39 (2008) 2464-2477
64. Mitra S., and Banka H. Multi-objective evolutionary biclustering of gene expression data. Pattern Recognition 39 (2006) 2464-2477
65. Jain A.K., and Dubes R.C. Algorithms for Clustering Data (1988), Prentice-Hall, NJ
66. Torkkola K., Gardner R.M., Kaysser-Kranich T., and Ma C. Self-organizing maps in mining gene expression data. Information Sciences 139 (2001) 79-96
67. Tamayo P., Slonim D., Mesirov J., Zhu Q., Kitareewan S., Smitrovsky E., Lander E.S., and Golub T.R. Interpreting patterns of gene expression with self-organizing maps: methods and applications to hematopoietic differentiation. Proceedings of National Academy of Sciences of the United States of America 96 (1999) 2907-2912
68. Törönen P., Kolehmainen M., Wong G., and Castrén E. Analysis of gene expression data using self-organizing maps. FEBS Letters 451 (1999) 142-146
69. Herrero J., Valencia A., and Dopazo J. A hierarchical unsupervised growing neural network for clustering gene expression patterns. Bioinformatics 17 (2001) 126-136
70. Cho S.B., and Ryu J. Classifying gene expression data of cancer using classifier ensemble with mutually exclusive features. Proceedings of the IEEE 90 (2002) 1744-1753
71. F. Chu, W. Xie, L. Wang, Gene selection and cancer classification using a fuzzy neural network, in: Proceedings of 2004 Annual Meeting of the North American Fuzzy Information Processing Society (NAFIPS 2004), vol. 2, 2004, pp. 555-559.
72. Futschik M.E., Reeve A., and Kasabov N. Evolving connectionist systems for knowledge discovery from gene expression data of cancer tissue. Artificial Intelligence in Medicine 28 (2003) 165-189
73. Deb K., Agarwal S., Pratap A., and Meyarivan T. A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6 (2002) 182-197