Bioinformatics tools in predictive ecology: Applications to fisheries

Philosophical Transactions of the Royal Society B: Biological Sciences

Volumn 367, Issue 1586, 2012, Pages 279-290

  Tucker, Allan ^a   Duplisea, Daniel ^b  

^a BRUNEL UNIVERSITY   (United Kingdom)

^b MAURICE LAMONTAGNE INSTITUTE   (Canada)

Author keywords

Bayesian networks; Bioinformatics; Classification; Dynamic models; Fisheries management

Indexed keywords

ALGORITHM; BAYESIAN ANALYSIS; BIOINFORMATICS; CLASSIFICATION; DATA SET; FISHERY; FISHERY MANAGEMENT; FISHERY MODELING; GENE EXPRESSION; NUMERICAL MODEL; PREDICTION; PROTEIN;

EID: 82755174160     PISSN: 09628436     EISSN: 14712970     Source Type: Journal    
DOI: 10.1098/rstb.2011.0184     Document Type: Article

References (60)

1. Bishop, C. M. 2006 Pattern recognition and machine learning. New York, NY: Springer
2. Hand, D. J., Mannila, H. & Smyth, P. 2001 Principles of data mining. Cambridge, MA: MIT Press
3. Berthold, M. R., Borgelt, C., Hoppner, F. & Klawonn, F. 2010 Guide to intellgent data analysis. Berlin, Germany: Springer
4. Peek, N., Combi, C. & Tucker, A. 2009 Biomedical data mining. Methods Inform. Med. 48, 225-228
5. Eisen, M., Spellman, P., Brown, P. & Botstein, D. 1998 Cluster analysis and display of genome-wide expression patterns. Proc. Natl Acad. Sci. USA 95, 8-25. (doi:10. 1073/pnas.95.25.14863)
6. Swift, S., Tucker, A., Liu, X., Martin, N., Orengo, C. & Kellam, P. 2004 Consensus clustering and functional interpretation of gene expression data. Genome Biol. 5, R94
7. Segal, E., Friedman, N., Koller, D. & Regev, A. 2004 A module map showing conditional activity of expression modules in cancer. Nat. Genet. 36, 1090-1098. (doi:10.1038/ng1434)
8. Bar-Joseph, Z. et al. 2003 Computational discovery of gene modules and regulatory networks. Nat. Biotechnol. 21, 1337-1342. (doi:10.1038/nbt890)
9. Pittman, J. et al. 2004 Integrated modeling of clinical and gene expression information for personalized prediction of disease outcomes. Proc. Natl Acad. Sci. USA 101, 8431-8436. (doi:10.1073/pnas.0401736101)
10. Inza, I., Larrañaga, P., Blanco, R. & Cerrolaza, A. J. 2004 Filter versus wrapper gene selection approaches in DNA microarray domains. Artif. Intell. Med. 31, 91-103. (doi:10.1016/j.artmed.2004.01.007)
11. Kim, S., Imoto, S. & Miyano, S. 2003 Inferring gene networks from time series microarray data using dynamic Bayesian networks. Brief. Bioinform. 4, 228-235. (doi:10.1093/bib/4.3.228)
12. Pe'er, D., Regev, A., Elidan, G. & Friedman, N. 2001 Inferring subnetworks from perturbed expression profiles. In Proc. 9th Int. Conf. Intelligent Systems for Molecular Biology (ISMB 2001), Copenhagen, Denmark, July 2001. Oxford, UK: Oxford Journals
13. Li, H., Xuan, J., Wang, Y. & Zhan, M. 2008 Inferring regulatory networks. Front. Biosci. 13, 263-275. (doi:10.2741/2677)
14. Hochachka, W. M., Caruana, R., Fink, D., Munson, A., Riedewald, M., Sorokina, D. & Kelling, S. 2007 Datamining discovery of pattern and process in ecological systems. J. Wildlife Manage. 71, 2427-2437. (doi:10. 2193/2006-503)
15. Friedman, N., Linial, M., Nachman, I. & Pe'er, D. 2000 Using Bayesian networks to analyze expression data. J. Comput. Biol. 7, 601-620. (doi:10.1089/10665270 0750050961)
16. Pe'er, D., Tanay, A. & Regev, A. 2006 MinReg: a scalable algorithm for learning parsimonious networks in yeast and mammals. J. Mach. Learning Res. 7, 167-189
17. Hartemink, A. J., Gifford, D., Jaakkola, T. & Young, R. 2002 Bayesian methods for elucidating genetic regulatory networks. IEEE Intell. Syst. 17, 37-43
18. Spirtes, P., Glymour, C. & Scheines, R. 2000 Causation, prediction, and search. Cambridge, MA: MIT Press
19. Pearl, J. 2001 Causality: models, reasoning, and inference. Cambridge, UK: Cambridge University Press
20. Cooper, G. F. & Herskovitz, E. 1992 A Bayesian method for the induction of probabilistic networks from data. Mach. Learning 9, 309-347. (doi:10.1007/BF00994110)
21. Janžura, M. & Nielsen, J. 2006 A simulated annealingbased method for learning Bayesian networks from statistical data: research articles. Int. J. Intell. Syst. 21, 335-348. (doi:10.1002/int.20138)
22. Steele, E., Tucker, A., Hoen, & Schuemie, M. J. 2009 Literature-based priors for gene regulatory networks. Bioinformatics 25, 1768-1774. (doi:10.1093/ bioinformatics/btp277)
23. Larrañaga, P., Poza, M., Yurramendi, Y., Murga, R. H. & Kuijpers, C. M. H. 1996 Structure learning of Bayesian networks by genetic algorithms: a performance analysis of control parameters. IEEE Trans. Pattern Anal. Mach. Intell. 18, 912-926. (doi:10.1109/34.537345)
24. Langley, P., Iba, W. & Thompson, K. 1992 An analysis of Bayesian classifiers. In Proc. 10th Natl Conf. Artificial Intelligence, San Jose, CA, July 1992, pp. 223-228. Menlo Park, CA: AAAI Press
25. Ghahramani, Z. 1998 Learning dynamic Bayesian networks. In Adaptive processing of sequences and data structures. Lecture Notes in Artificial Intelligence, pp. 168-197. Berlin, Germany: Springer
26. Friedman, N., Geiger, D. & Goldszmidt, M. 1997 Bayesian network classifiers. Mach. Learning 29, 131-163. (doi:10.1023/A:1007465528199)
27. Munch, K., Gardner, P. P., Arctander, P. & Krogh, A. 2006 A hidden Markov model approach for determining expression from genomic tiling microarrays. BMC Bioinform. 7, 239. (doi:10.1186/1471-2105-7-239)
28. Castelo, R. & Siebes, A. 2000 Priors on network structures: biasing the search for Bayesian networks. Int. J. Approximate Reasoning 24, pp. 39-57. (doi:10. 1016/S0888-613X(99)00041-9)
29. Imoto, S., Goto, T. & Miyano, S. 2002 Estimation of genetic networks and functional structures between genes by using Bayesian networks and nonparametric regression. In Pacific Symposium on Biocomputing, Lihue, Hawaii, January 2002, vol. 7, pp. 175-186
30. Werhli, A. V. & Husmeier, D. 2007 Reconstructing gene regulatory networks with Bayesian networks by combining expression data with multiple sources of prior knowledge. Stat. Appl. Genet. Mol. Biol. 6, art. 15 (doi:10.2202/1544-6115.1282)
31. Yauk, C., Nerndt, M. L., Williams, A. & Douglas, G. 2004 Comprehensive comparison of six microarray technologies. Nucleic Acids Res. 32, e124. (doi:10.1093/nar/ gnh123)
32. Kuo, W. P., Jenssen, T. K., Butte, A. J., Ohno-Machado, L. & Kohane, I. S. 2002 Analysis of matched mRNA measurements from two different microarray technologies. Bioinformatics 18, 405-412. (doi:10.1093/ bioinformatics/18.3.405)
33. Jarvinen, A. K., Hautaniemi, S., Edgren, H., Auvinen, P., Saarela, J., Kallionoemi, O. P. & Monni, O. 2004 Are data from different gene expression microarrays comparable? Genomics 83, 1164-1168. (doi:10.1016/j.ygeno. 2004.01.004)
34. Steele, E. & Tucker, A. 2008 Consensus and metaanalysis regulatory networks for combining multiple microarray gene expression datasets. J. Biomed. Inform. 41, 914-926. (doi:10.1016/j.jbi.2008.01.011)
35. Steele, E. & Tucker, A. 2009 Selecting and weighting data for building consensus gene regulatory networks. In Advances in intelligent data analysis VIII, vol. LNCS 5772, pp. 190-201. Berlin, Germany: Springer
36. Anvar, S. Y., Hoen, PA. & Tucker, A. 2010 The identification of informative genes from multiple datasets with increasing complexity. BMC Bioinform. 11, 32. (doi:10. 1186/1471-2105-11-32)
37. Anvar, Y., Tucker, A., Vinciotti, V., Venema, A., van Ommen, G. J. B., van der Maarel, S. M., Raz, V. & Hoen, P. A. C. In press. Interspecies translation of disease networks increases robustness and predictive accuracy. PLOS Comput. Biol
38. Garrison, L. P. & Link, J. S. 2000 Dietary guild structure of the fish community in the northeast united states continental shelf ecosystem. Mar. Ecol. Prog. Ser. 202, 231-240. (doi:10.3354/meps202231)
39. Jaio, Y. 2009 Regime shift in marine ecosystems and implications for fisheries management, a review. Rev. Fish Biol. Fish. 19, 177-191. (doi:10.1007/s11160-008-9096-8)
40. Bakun, A. 2006 Wasp-waist populations and marine ecosystem dynamics: navigating the 'predator pit' topographies. Prog. Oceanogr. 68, 271-288. (doi:10. 1016/j.pocean.2006.02.004)
41. Marcot, B. G., Steventon, J. D., Sutherland, G. D. & McCann, R. K. 2006 Guidelines for developing and updating Bayesian belief networks applied to ecological modeling and conservation. Can. J. For. Res. 36, 3063-3074. (doi:10.1139/x06-135)
42. Hammond, T. R. & O'Brien, C. M. 2001 An application of the Bayesian approach to stock assessment model uncertainty. ICES J. Mar. Sci. 58, 648-656. (doi:10. 1006/jmsc.2001.1051)
43. Marcot, B. G., Holthausen, R. S., Raphael, M. G., Rowland, M. M. & Wisdom, M. J. 2001 Using Bayesian belief networks to evaluate fish and wildlife population viability under land management alternatives from an environmental impact statement. For. Ecol. Manage. 153, 29-42. (doi:10.1016/S0378-1127(01)00452-2)
44. SEIS 2008. European commission: towards a shared environmental information system. See http://ec.europa. eu/environment/seis/index.htm
45. Thrush, S., Giovani, C. & Hewitt, J. E. 2008 Complex positive connections between functional groups are revealed by neural network analysis of ecological timeseries. Am. Nat. 171, 669-677
46. Efron, B. & Tibshirani, R. 1995 Cross-validation and the bootstrap: estimating the error rate of a prediction rule. Technical Report no. TR-477, Department of Statistics, Stanford University, Stanford, CA, USA
47. Liang, S., Fuhrman, S. & Somogyi, R. 1998 Reveal, a general reverse engineering algorithm for inference of genetic network architectures. In Pacific Symposium on Biocomputing, Maui, Hawaii, January 1998, vol. 3, pp. 18-29
48. Kirkpatrick, S., Gelatt, C. D. & Vecchi, M. P. 1983 Optimization by simulated annealing. Science 220, 671-680. (doi:10.1126/science.220.4598.671)
49. Kane, J. 2007 Zooplankton abundance trends on Georges Bank, 1977-2004. ICES J. Mar. Sci. 64, 909-919. (doi:10.1093/icesjms/fsm066)
50. Beaugrand, G., Brander, K., Lindley, J. A., Souissi, S. & Reid, P. C. 2000 Plankton effect on cod recruitment in the north sea. Nature 426, 661-664. (doi:10.1038/ nature02164)
51. Swain, D. P. & Sinclair, A. 2000 Pelagic fishes and the cod recruitment dilemma in the northwest atlantic. Can. J. Fish. Aquat. Sci. 57, 1321-1325. (doi:10.1139/ f00-104)
52. Fogarty, M. J. & Murawski, S. A. 1998 Large-scale disturbance and the structure of marine systems: fishery impacts on Georges Bank. Ecol. Appl. 8, S6-S22
53. Frisk, M. G., Miller, T. J., Martell, S. J. D. & Sosebee, K. 2008 New hypothesis helps explain elasmobranch 'outburst' on Georges Bank in the 1980s. Ecol. Appl. 18, 234-245. (doi:10.1890/06-1392.1)
54. Bundy, A. 2001 Fishing on ecosystems: the interplay of fishing and predation in Newfoundland and Labrador. Can. J. Fish. Aquat. Sci. 58, 1153-1167
55. Myers, R. A. & Worm, B. 2003 Rapid worldwide depletion of predatory fish communities. Nature 423, 280-283. (doi:10.1038/nature01610)
56. Frank, K., Petrie, B., Choi, J. & Leggett,W. 2005 Trophic cascades in a formerly cod-dominated ecosystem. Science 308, 1621-1623. (doi:10.1126/science.1113075)
57. Petrie, B., Frank, K. T., Shackell, N. L. & Leggett, W. C. 2009 Structure and stability in exploited marine fish communities: quantifying critical transitions. Fish. Oceanogr. 18, 83-101. (doi:10.1111/j.1365-2419.2009. 00500.x)
58. Duplisea, D. E. & Blanchard, F. 2005 Relating species and community dynamics in an heavily exploited marine fish community. Ecosystems 8, 899-910. (doi:10.1007/s10021-005-0011-z)
59. Choi, J. S., Frank, K. T., Petrie, B. D. & Leggett, W. C. 2005 Integrated assessment of a large marine ecosystem: a case study of the devolution of the Eastern Scotian shelf. Oceanogr. Mar. Biol. 43, 47-67
60. Carpenter, S. R. et al. 2011 Early warnings of regime shifts: a whole-ecosystem experiment. Science 27, 1079-1082. (doi:10.1126/science.1203672)