On the optimization of classes for the assignment of unidentified reading frames in functional genomics programmes: The need for machine learning

Trends in Biotechnology

Volumn 18, Issue 3, 2000, Pages 93-98

  Kell, Douglas B ^a   King, Ross D ^a  

^a ABERYSTWYTH UNIVERSITY   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

BACTERIAL GENE; BACTERIAL GENETICS; DATA ANALYSIS; GENE FUNCTION; GENE SEQUENCE; GENOME; INTERNET; MYCOBACTERIUM TUBERCULOSIS; NONHUMAN; PRIORITY JOURNAL; REVIEW;

BACTERIA (MICROORGANISMS); MYCOBACTERIUM; MYCOBACTERIUM TUBERCULOSIS;

EID: 0034160040     PISSN: 01677799     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0167-7799(99)01407-9     Document Type: Note

References (72)

1. 1 Brent, R. (1999) Functional genomics: learning to think about gene expression data. Curr. Biol. 9, R338-R341
2. 2 Everitt, B.S. and Dunn, G. (1991) Applied Multivariate Data Analysis, Edward Arnold
3. 3 Everitt, B.S. (1993) Cluster Analysis (3rd edn), Edward Arnold
4. 4 Hinton, J.C.D. (1997) The Escherichia coli genome sequence: the end of an era or the start of the FUN? Mol. Microbiol. 26, 417-422
5. 5 Bork, P. et al. (1998) Predicting function: from genes to genomes and back. J. Mol. Biol. 283, 707-725
6. 6 Chu, S. et al. (1998) The transcriptional program of sporulation in budding yeast. Science 282, 699-705
7. 7 DeRisi, J.L. et al. (1997) Exploring the metabolic and genetic control of gene expression on a genomic scale. Science 278, 680-686
8. 8 Brown, P.O. and Botstein, D. (1999) Exploring the new world of the genome with DNA microarrays. Nat. Genet. 21, 33-37
9. 9 Lemieux, B. et al. (1998) Overview of DNA chip technology. Mol. Breed. 4, 277-289
10. 10 Schena, M. et al. (1998) Microarrays: biotechnology's discovery platform for functional genomics. Trends Biotechnol. 16, 301-306
11. 11 Wilkins, M.R. et al. (1997) Proteome Research: New Frontiers in Functional Genomics, Springer-Verlag
12. 12 Blackstock, W.P. and Weir, M.P. (1999) Proteomics: quantitative and physical mapping of cellular proteins. Trends Biotechnol. 17, 121-127
13. 13 Oliver, S.G. et al. (1998) Systematic functional analysis of the yeast genome. Trends Biotechnol. 16, 373-378
14. 14 Shoemaker, D.D. et al. (1996) Quantitative phenotypic analysis of yeast deletion mutants using a highly parallel molecular bar-coding strategy. Nat. Genet. 14, 450-456
15. 15 Riley, M. (1993) Functions of the gene products of Escherichia coli. Microbiol. Rev. 57, 862-952
16. 16 Riley, M. and Labedan, B. (1996) Escherichia coli gene products: physiological functions and common ancestries. In Escherichia coli and Salmonella: Cellular and Molecular Biology (2nd edn) (Neidhardt, F. et al., eds), pp. 2118-2202, American Society for Microbiology
17. 17 Karp, P.D. et al. (1999) Integrated pathway-genome databases and their role in drug discovery. Trends Biotechnol. 17, 275-281
18. 18 Michal, G. (1999) Biochemical Pathways: an Atlas of Biochemistry and Molecular Biology, Wiley
19. 19 Duda, R.O. and Hart, P.E. (1973) Pattern Classification and Scene Analysis, Wiley
20. 20 Jain, A.K. and Dubes, R.C. (1988) Algorithms for Clustering Data, Prentice Hall
21. 21 Therrien, C.W. (1989) Decision Estimation and Classification: an Introduction to Pattern Recognition and Related Topics, Wiley
22. 22 Rich, E. and Knight, K. (1991) Artificial Intelligence, McGraw-Hill
23. 23 Weiss, S.H. and Kulikowski, C.A. (1991) Computer Systems that Learn: Classification and Prediction Methods from Statistics, Neural Networks, Machine Learning, and Expert Systems, Morgan Kaufmann Publishers
24. 24 Fukunaga, K. (1992) Introduction to Statistical Pattern Recognition, Academic Press
25. 25 Michie, D. et al. (1994) Machine learning: neural and statistical classification. In Ellis Horwood Series in Artificial Intelligence (Campbell, J., ed.), Ellis Horwood
26. 26 Bishop, C.M. (1995) Neural Networks for Pattern Recognition, Clarendon Press
27. 27 Livingstone, D. (1995) Data Analysis for Chemists, Oxford University Press
28. 28 Ripley, B.D. (1996) Pattern Recognition and Neural Networks, Cambridge University Press
29. 29 Mitchell, T.M. (1997) Machine Learning, McGraw-Hill
30. 30 Wen, X.L. et al. (1998) Large-scale temporal gene expression mapping of central nervous system development. Proc. Natl. Acad. Sci. U. S. A. 95, 334-339
31. 31 Eisen, M.B. et al. (1998) Cluster analysis and display of genomewide expression patterns. Proc. Natl. Acad. Sci. U. S. A. 95, 14863-14868
32. 32 Tamayo, P. et al. (1999) Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. Proc. Natl. Acad. Sci. U. S. A. 96, 2907-2912
33. 33 Somogyi, R. (1999) Making sense of gene-expression data. Pharmainformatics (a Trends Guide) 17-24
34. 34 Kao, C.M. (1999) Functional genomic technologies: creating new paradigms for fundamental and applied biology. Biotechnol. Prog. 15, 304-311
35. 35 Golub, T.R. et al. (1999) Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286, 531-537
36. 36 Bäck, T. et al. (1997) Handbook of Evolutionary Computation, IOP Publishing/Oxford University Press
37. 37 Breiman, L. et al. (1984) Classification and Regression Trees, Wadsworth International
38. 38 Quinlan, J.R. (1993) C4.5: Programs for Machine Learning, Morgan Kaufmann
39. 39 Beadle, G.W. and Tatum, E.L. (1941) Genetic control of biochemical reactions in Neurospora. Proc. Natl. Acad. Sci. U. S. A. 17, 499-506
40. 40 Tanksley, S.D. (1993) Mapping polygenes. Annu. Rev. Genet. 27, 205-233
41. 41 Bezdek, J.C. and Pal, S.K. (1992) Fuzzy Models for Pattern Recognition: Methods that Search for Structures in Data, IEEE Press
42. 42 Li, H-X. and Yen, V.C. (1995) Fuzzy Sets and Fuzzy Decision-making, CRC Press
43. 43 Ruspini, E.H. et al. (1998) Handbook of Fuzzy Computation, Institute of Physics Publishing
44. 44 Sokal, R.R. (1985) Computer-assisted Bacterial Systematics (Goodfellow, M. et al., eds), Society for General Microbiology, Academic Press
45. 45 Wallace, C.S. and Boulton, D.M. (1968) An information measure for classification. Comput. J. 11, 185-195
46. 46 Li, M. and Vitányi, P. (1997) An Introduction to Kolmogorov Complexity and its Applications, Springer-Verlag
47. 47 Sokal, R.R. and Sneath, P.H.A. (1963) Principles of Numerical Taxonomy, Freeman
48. 48 Sneath, P.H.A. and Sokal, R.R. (1973) Numerical Taxonomy, Freeman
49. 49 Kell, D.B. et al. (1998) Viability and activity in readily culturable bacteria: a review and discussion of the practical issues. Antonie Van Leeuwenhoek Int. J. Gen. Mol. Microbiol. 73, 169-187
50. 50 Miyamoto, M.M. and Cracraft, J. (1991) Phylogenetic Analysis of DNA Sequences, Oxford University Press
51. 51 Hillis, D.M. et al. (1996) Molecular Systematics, Sinauer Associates
52. 52 Cornish-Bowden, A. and Cárdenas, M.L. (1990) Control of Metabolic Processes, Plenum Press
53. 53 Ovádi, J. (1995) Cell Architecture and Metabolic Channeling, Springer-Verlag
54. 54 Fell, D.A. (1996) Understanding the Control of Metabolism, Portland Press
55. 55 Heinrich, R. and Schuster, S. (1996) The Regulation of Cellular Systems, Chapman & Hall
56. 56 Teusink, B. et al. (1998) Methods in Microbiology: Yeast Gene Analysis (Tuite, M.F. and Brown, A.J.P., eds), Academic Press
57. 57 Kell, D.B. and Mendes, P. (2000) Snapshots of systems: metabolic control analysis and biotechnology in the post-genomic era. In Technological and Medical Implications of Metabolic Control Analysis (Cornish-Bowden, A. and Cárdenas, M.L., eds), pp. 3-25, Kluwer Academic Publishers
58. 58 Lee, K.H. et al. (1996) Deregulated expression of cloned transcription factor E2F-1 in Chinese hamster ovary cells shifts protein patterns and activates growth in protein-free medium. Biotechnol. Bioeng. 50, 273-279
59. 59 Knuth, D.E. (1973) The Art of Computer Programming: Fundamental Algorithms, Addison-Wesley
60. 60 Gilbert, R.J. et al. (1999) Genetic programming as an analytical tool for metabolome data, In Late-breaking Papers of EuroGP-99 (Langdon, W.B. et al., eds), pp. 23-33, Software Engineering
61. 61 Muggleton, S.H. (1990) Inductive Logic Programming. New Generat. Comput. 8, 295-318
62. 62 Mendes, P. and Kell, D.B. (1996) On the analysis of the inverse problem of metabolic pathways using artificial neural networks. BioSystems 38, 15-28
63. 63 Mendes, P. and Kell, D.B. (1998) Non-linear optimization of biochemical pathways: applications to metabolic engineering and parameter estimation. Bioinformatics 14, 869-883
64. 64 Pfeiffer, T. et al. (1999) METATOOL: for studying metabolic networks. Bioinformatics 15, 251-257
65. 65 Davey, H.M. et al. (1996) Oscillatory, stochastic and chaotic growth rate fluctuations in permittistatically-controlled yeast cultures. BioSystems 39, 43-61
66. 66 Haldane, J.B.S. (1927) Possible Worlds, and Other Essays, Chatto & Windus
67. 67 Brenner, S.E. (1999) Errors in genome annotation. Trends Genet. 15, 132-133
68. 68 Hilsenbeck, S.G. et al. (1999) Statistical analysis of array expression data as applied to the problem of tamoxifen resistance. J. Natl. Cancer Inst. 91, 453-459
69. 69 Wittes, J. and Friedman, H.P. (1999) Searching for evidence of altered gene expression: a comment on statistical analysis of microarray data. J. Natl. Cancer Inst. 91, 400-401
70. 70 Kell, D.B. and Sonnleitner, B. (1995) GMP - good modelling practice: an essential component of good manufacturing practice. Trends Biotechnol. 13, 481-492
71. 71 Oldroyd, D. (1986) The Arch of Knowledge: an Introduction to the History of the Philosophy and Methodology of Science, Methuen
72. 72 Langley, P. et al. (1987) Scientific Discovery: Computational Exploration of the Creative Processes, MIT Press