Robust Machine Learning Algorithms Predict MicroRNA Genes and Targets

Methods in Enzymology

Volumn 427, Issue , 2007, Pages 25-49

  Sætrom, Pål ^a,b,c   Snøve Jr , Ola ^a,b,c  

^a Interagon AS   (Norway)

^b NORWEGIAN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Norway)

^c CITY OF HOPE NATIONAL MEDICAL CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

MICRORNA;

3' UNTRANSLATED REGION; ALGORITHM; CLASSIFIER; CODON; COMPUTER PREDICTION; DIAGNOSTIC VALUE; EXPERIMENTAL DESIGN; GENE EXPRESSION REGULATION; GENE TARGETING; GENETIC PROGRAMMING METHOD; HUMAN; INTERMETHOD COMPARISON; KERNEL MAPPING METHOD; MACHINE LEARNING; MATHEMATICAL MODEL; NONHUMAN; PRIORITY JOURNAL; PROBLEM SOLVING; REVIEW; RNA ANALYSIS; STOCHASTIC MODEL; SUPPORT VECTOR MACHINE; ARTICLE; GENETICS; LEARNING; STATISTICS;

ALGORITHMS; LEARNING; MICRORNAS; STOCHASTIC PROCESSES;

EID: 42949176776     PISSN: 00766879     EISSN: None     Source Type: Book Series    
DOI: 10.1016/S0076-6879(07)27002-8     Document Type: Review

References (73)

1. Bagga S., Bracht J., et al. Regulation by let-7 and lin-4 miRNAs results in target mRNA degradation. Cell 122 (2005) 553-563
2. Bajic V.B., Tan S.L., et al. Promoter prediction analysis on the whole human genome. Nat. Biotechnol. 22 (2004) 1467-1473
3. Baldi P., Brunak S., et al. Assessing the accuracy of prediction algorithms for classification: An overview. Bioinformatics 16 (2000) 412-424
4. Banzhaf W., Nordin P., et al. "Genetic programming: An introduction: On the automatic evolution of computer programs and its applications." (1997), Morgan Kaufmann Publishers, San Francisco
5. Bartel D.P. MicroRNAs: Genomics, biogenesis, mechanism, and function. Cell 116 (2004) 281-297
6. Behlke M.A. Progress towards in vivo use of siRNAs. Mol. Ther. 13 4 (2006) 644-670
7. Bentwich I. Prediction and validation of microRNAs and their targets. FEBS Lett. 579 (2005) 5904-5910
8. Bentwich I., Avniel A., et al. Identification of hundreds of conserved and nonconserved human microRNAs. Nat. Genet. 37 (2005) 766-770
9. Berezikov E., Cuppen E., et al. Approaches to microRNA discovery. Nat. Genet. 38 Suppl. (2006) S2-S7
10. Boutla A., Delidakis C., et al. Developmental defects by antisense-mediated inactivation of micro-RNAs 2 and 13 in Drosophila and the identification of putative target genes. Nucl. Acids Res. 31 (2003) 4973-4980
11. Breiman L. Bagging predictors. Machine Learning 24 2 (1996) 123-140
12. Brennecke J., Hipfner D.R., et al. Bantam encodes a developmentally regulated microRNA that controls cell proliferation and regulates the proapoptotic gene hid in Drosophila. Cell 113 (2003) 25-36
13. Brennecke J., Stark A., et al. Principles of microRNA-target recognition. PLoS Biol. 3 (2005) e85
14. Burges C.J.C. A tutorial on support vector machines for pattern recognition. Data Mining Knowl. Disc. 2 (1998) 121-167
15. D'Haeseleer P. How does gene expression clustering work?. Nat. Biotechnol. 23 (2005) 1499-1501
16. Dayhoff J.E., and DeLeo J.M. Artificial neural networks: Opening the black box. Cancer 91 (2001) 1615-1635
17. Didiano D., and Hobert O. Perfect seed pairing is not a generally reliable predictor for miRNA-target interactions. Nat. Struct. Mol. Biol. 13 (2006) 849-851
18. Doench J.G., and Sharp P.A. Specificity of microRNA target selection in translational repression. Genes Dev. 18 (2004) 504-511
19. Domingos P., and Pazzani M. On the optimality of the simple Bayesian classifier under zero-one loss. Machine Learning 29 (1997) 103-130
20. Esquela-Kerscher A., and Slack F.J. Oncomirs-microRNAs with a role in cancer. Nat. Rev. Cancer 6 (2006) 259-269
21. Freund Y., and Schapire R.E. A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. System Sci. 55 (1997) 119-139
22. Grad Y., Aach J., et al. Computational and experimental identification of C. elegans microRNAs. Mol. Cell 11 (2003) 1253-1263
23. Gregory R.I., Yan K.P., et al. The Microprocessor complex mediates the genesis of microRNAs. Nature 432 7014 (2004) 235-240
24. Griffiths-Jones S., Grocock R.J., et al. miRBase: MicroRNA sequences, targets and gene nomenclature. Nucleic Acids Res. 34 Database issue (2006) D140-D144
25. Guyon I., and Elisseeff A. An introduction to variable and feature selection. J. Machine Learning Res. 3 (2003) 1157-1182
26. Halaas A., Svingen B., et al. A recursive MISD architecture for pattern matching. Ieee Transact. Very Large Scale Integ. (Vlsi) Syst. 12 (2004) 727-734
27. Han J.J., Lee Y., et al. Molecular basis for the recognition of primary microRNAs by the Drosha-DGCR8 complex. Cell 125 (2006) 887-901
28. Hebsgaard S.M., Korning P.G., et al. Splice site prediction in Arabidopsis thaliana pre-mRNA by combining local and global sequence information. Nucleic Acids Res. 24 (1996) 3439-3452
29. Helvik S.A., and Snøve Jr. O. Reliable prediction of Drosha processing sites improves microRNA gene prediction. Bioinformatics 23 (2007) 142-149
30. Hertel J., Lindemeyer M., et al. The expansion of the metazoan microRNA repertoire. BMC Genomics 7 (2006) 25
31. Hertel J., and Stadler P.F. Hairpins in a haystack: Recognizing microRNA precursors in comparative genomics data. Bioinformatics 22 (2006) e197-e202
32. Hofacker I.L. Vienna RNA secondary structure server. Nucleic Acids Res. 31 (2003) 3429-3431
33. Jaakkola T., Diekhans M., et al. A discriminative framework for detecting remote protein homologies. J. Comput. Biol. 7 (2000) 95-114
34. Khan J., Wei J.S., et al. Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nat. Med. 7 (2001) 673-679
35. Kim S.K., Nam J.W., et al. miTarget: MicroRNA target-gene prediction using a Support Vector Machine. BMC Bioinformatics 7 (2006) 411
36. Koza J.R., Bennett H., et al. "Genetic Programming III: Darwinian Invention and Problem Solving." (1999), Morgan Kaufmann San Francisco
37. Krol J., Sobczak K., et al. Structural features of microRNA (miRNA) precursors and their relevance to miRNA biogenesis and small interfering RNA/short hairpin RNA design. J. Biol. Chem. 279 (2004) 42230-42239
38. Lagos-Quintana M., Rauhut R., et al. Identification of novel genes coding for small expressed RNAs. Science 294 (2001) 853-858
39. Lai E.C., Tomancak P., et al. Computational identification of Drosophila microRNA genes. Genome Biol. 4 (2003) R42
40. Lau N.C., Lim L.P., et al. An abundant class of tiny RNAs with probable regulatory roles in Caenorhabditis elegans. Science 294 (2001) 858-862
41. Lee R.C., and Ambros V. An extensive class of small RNAs in Caenorhabditis elegans. Science 294 (2001) 862-864
42. Lee Y., Ahn C., et al. The nuclear RNase III Drosha initiates microRNA processing. Nature 425 (2003) 415-419
43. Lewis B.P., Burge C.B., et al. Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell 120 (2005) 15-20
44. Lim L.P., Glasner M.E., et al. Vertebrate microRNA genes. Science 299 (2003) 1540
45. Lim L.P., Lau N.C., et al. The microRNAs of Caenorhabditis elegans. Genes Dev. 17 (2003) 991-1008
46. Lim L.P., Lau N.C., et al. Microarray analysis shows that some microRNAs downregulate large numbers of target mRNAs. Nature 433 (2005) 769-773
47. Lu J., Getz G., et al. MicroRNA expression profiles classify human cancers. Nature 435 (2005) 834-838
48. Martin J.K., and Hirschberg D.S. Small sample statistics for classification error rates I: Error rate measurements. Technical Report 96-21 (1996), University of California, Irvine
49. In: Meir R., and Rätsch G. (Eds). "An Introduction to Boosting and Leveraging. Advanced Lectures on Machine Learning." (2003), Springer-Verlag, Berlin
50. Müller K.R., Mika S., et al. An introduction to kernel-based learning algorithms. IEEE Trans. Neural Networks 12 (2001) 181-201
51. Nam J.W., Shin K.R., et al. Human microRNA prediction through a probabilistic co-learning model of sequence and structure. Nucleic Acids Res. 33 (2005) 3570-3581
52. Ohler U., Yekta S., et al. Patterns of flanking sequence conservation and a characteristic upstream motif for microRNA gene identification. RNA 10 (2004) 1309-1322
53. Olsen P.H., and Ambros V. The lin-4 regulatory RNA controls developmental timing in Caenorhabditis elegans by blocking lin-14 protein synthesis after the initiation of translation. Dev. Biol. 216 (1999) 671-680
54. Pasquinelli A.E., Reinhart B.J., et al. Conservation of the sequence and temporal expression of let-7 heterochronic regulatory RNA. Nature 408 (2000) 86-89
55. Rajewsky N., and Socci N.D. Computational identification of microRNA targets. Dev. Biol. 267 (2004) 529-535
56. Rehmsmeier M., Steffen P., et al. Fast and effective prediction of microRNA/target duplexes. RNA 10 (2004) 1507-1517
57. Reinhart B.J., Slack F.J., et al. The 21-nucleotide let-7 RNA regulates developmental timing in Caenorhabditis elegans. Nature 403 (2000) 901-906
58. Saetrom O., Snove Jr. O., et al. Weighted sequence motifs as an improved seeding step in microRNA target prediction algorithms. RNA 11 (2005) 995-1003
59. Saetrom P. Predicting the efficacy of short oligonucleotides in antisense and RNAi experiments with boosted genetic programming. Bioinformatics 20 (2004) 3055-3063
60. Saetrom P., Snove O., et al. Conserved MicroRNA characteristics in mammals. Oligonucleotides 16 (2006) 115-144
61. Safavian S.R., and Landgrebe D. A survey of decision tree classifier methodology. IEEE Transact. Systems, Man Cybernet. 21 (1991) 660-674
62. Salzberg S. On comparing classifiers: Pitfalls to avoid and a recommended approach. Data Mining Knowledge Disc. 1 (1997) 317-328
63. Sewer A., Paul N., et al. Identification of clustered microRNAs using an ab initio prediction method. BMC Bioinformatics 6 (2005) 267
64. Smith T.F., and Waterman M.S. Identification of common molecular subsequences. J. Mol. Biol. 147 (1981) 195-197
65. Stone M. Cross-validatory choice and assessment of statistical predictions. J. Royal Stat. Soc. Series B (Methodological) 36 (1974) 111-147
66. Vapnik V.N. "Statistical Learning Theory." (1998), Wiley-Interscience, New York
67. Wightman B., Ha I., et al. Posttranscriptional regulation of the heterochronic gene lin-14 by lin-4 mediates temporal pattern formation in C. elegans. Cell 75 (1993) 855-862
68. Wu L., Fan J., et al. MicroRNAs direct rapid deadenylation of mRNA. Proc. Natl. Acad. Sci. USA 103 (2006) 4034-4039
69. Xue C., Li F., et al. Classification of real and pseudo microRNA precursors using local structure-sequence features and support vector machine. BMC Bioinformatics 6 (2005) 310
70. Yekta S., Shih I.H., et al. MicroRNA-directed cleavage of HOXB8 mRNA. Science 304 (2004) 594-596
71. Zeng Y., and Cullen B.R. Sequence requirements for microRNA processing and function in human cells. RNA 9 (2003) 112-123
72. Zeng Y., and Cullen B.R. Structural requirements for pre-microRNA binding and nuclear export by Exportin 5. Nucleic Acids Res. 32 (2004) 4776-4785
73. Zeng Y., Yi R., et al. Recognition and cleavage of primary microRNA precursors by the nuclear processing enzyme Drosha. EMBO J. 24 1 (2005) 138-148