Unifying generative and discriminative learning principles

BMC Bioinformatics

Volumn 11, Issue , 2010, Pages

  Keilwagen, Jens ^a   Grau, Jan ^b   Posch, Stefan ^b   Strickert, Marc ^a   Grosse, Ivo ^b  

^a LEIBNIZ INSTITUTE OF PLANT GENETICS AND CROP PLANT RESEARCH IPK   (Germany)

^b MARTIN LUTHER UNIVERSITY HALLE WITTENBERG   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

BIOINFORMATICS APPLICATIONS; COMPUTATIONAL APPROACH; CONDITIONAL LIKELIHOOD; DISCRIMINATIVE LEARNING; MAXIMUM A POSTERIORI; OPEN-SOURCE LIBRARIES; TRANSCRIPTION FACTOR BINDING; TRANSCRIPTION FACTOR BINDING SITES;

BIOINFORMATICS; GENES; TRANSCRIPTION FACTORS;

MAXIMUM LIKELIHOOD;

VERTEBRATA;

DNA;

ALGORITHM; ARTICLE; AUTOMATED PATTERN RECOGNITION; CHEMISTRY; DISCRIMINANT ANALYSIS; GENOME; GENOMICS; INFORMATION RETRIEVAL; METABOLISM; METHODOLOGY; STATISTICAL MODEL;

ALGORITHMS; DISCRIMINANT ANALYSIS; DNA; GENOME; GENOMICS; INFORMATION STORAGE AND RETRIEVAL; LIKELIHOOD FUNCTIONS; PATTERN RECOGNITION, AUTOMATED;

EID: 77951165359     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-11-98     Document Type: Article

References (47)

1. Kel AE, Gössling E, Reuter I, Cheremushkin E, Kel-Margoulis OV, Wingender E. MATCH: A tool for searching transcription factor binding sites in DNA sequences. Nucleic Acids Res 2003, 31(13):3576-3579. 10.1093/nar/gkg585, 169193, 12824369.
2. Barash Y, Elidan G, Friedman N, Kaplan T. Modeling Dependencies in Protein-DNA Binding Sites. In proceedings of Seventh Annual International Conference on Computational Molecular Biology 2003, 28-37.
3. Sonnenburg S, Zien A, Rätsch G. ARTS: accurate recognition of transcription starts in human. Bioinformatics 2006, 22(14):e472-e480. 10.1093/bioinformatics/btl250, 16873509.
4. Abeel T, Peer Y, Saeys Y. Toward a gold standard for promoter prediction evaluation. Bioinformatics 2009, 25(12):i313-i320. 10.1093/bioinformatics/btp191, 2687945, 19478005.
5. Burge C, Karlin S. Prediction of complete gene structures in human genomic DNA. J Mol Biol 1997, 268:78-94. 10.1006/jmbi.1997.0951, 9149143.
6. Salzberg SL. A method for identifying splice sites and translational start sites in eukaryotic mRNA. Comput Appl Biosci 1997, 13(4):365-376.
7. Yeo G, Burge CB. Maximum Entropy Modeling of Short Sequence Motifs with Applications to RNA Splicing Signals. Journal of Computational Biology 2004, 11(2-3):377-394. 10.1089/1066527041410418, 15285897.
8. Segal E, Fondufe-Mittendorf Y, Chen L, Thåström A, Field Y, Moore IK, Wang JPZ, Widom J. A genomic code for nucleosome positioning. Nature 2006, 442(7104):772-778. 10.1038/nature04979, 2623244, 16862119.
9. Peckham HE, Thurman RE, Fu Y, Stamatoyannopoulos JA, Noble WS, Struhl K, Weng Z. Nucleosome positioning signals in genomic DNA. Genome Res 2007, 17(8):1170-1177. 10.1101/gr.6101007, 1933512, 17620451.
10. Lewis BP, hung Shih I, Jones-Rhoades MW, Bartel DP, Burge CB. Prediction of Mammalian MicroRNA Targets. Cell 2003, 115(7):787-798. 10.1016/S0092-8674(03)01018-3, 14697198.
11. Maragkakis M, Reczko M, Simossis VA, Alexiou P, Papadopoulos GL, Dalamagas T, Giannopoulos G, Goumas G, Koukis E, Kourtis K, Vergoulis T, Koziris N, Sellis T, Tsanakas P, Hatzigeorgiou AG. DIANA-microT web server: elucidating microRNA functions through target prediction. Nucl Acids Res 2009, 37(suppl 2):W273-276. 10.1093/nar/gkp292, 2703977, 19406924.
12. Kim TH, Abdullaev ZK, Smith AD, Ching KA, Loukinov DI, Green RD, Zhang MQ, Lobanenkov VV, Ren B. Analysis of the vertebrate insulator protein CTCF-binding sites in the human genome. Cell 2007, 128(6):1231-1245. 10.1016/j.cell.2006.12.048, 2572726, 17382889.
13. Staden R. Computer methods to locate signals in nucleic acid sequences. NAR 1984, 12:505-519. 10.1093/nar/12.1Part2.505, 321067, 6364039.
14. Stormo G, Schneider T, Gold L, Ehrenfeucht A. Use of the 'perceptron' algorithm to distinguish translational initiation sites. NAR 1982, 10:2997-3010. 10.1093/nar/10.9.2997, 320670, 7048259.
15. Zhang M, Marr T. A weight array method for splicing signal analysis. Comput Appl Biosci 1993, 9(5):499-509.
16. Yakhnenko O, Silvescu A, Honavar V. Discriminatively Trained Markov Model for Sequence Classification. ICDM '05: Proceedings of the Fifth IEEE International Conference on Data Mining, Washington, DC, USA: IEEE Computer Society 2005, 498-505.
17. Keilwagen J, Grau J, Posch S, Grosse I. Recognition of splice sites using maximum conditional likelihood. LWA: Lernen - Wissen - Abstraktion 2007, 67-72. Hinneburg A.
18. Cai D, Delcher A, Kao B, Kasif S. Modeling splice sites with Bayes networks. Bioinformatics 2000, 16(2):152-158. 10.1093/bioinformatics/16.2.152, 10842737.
19. Ben-Gal I, Shani A, Gohr A, Grau J, Arviv S, Shmilovici A, Posch S, Grosse I. Identification of transcription factor binding sites with variable-order Bayesian networks. Bioinformatics 2005, 21(11):2657-2666. 10.1093/bioinformatics/bti410, 15797905.
20. Culotta A, Kulp D, McCallum A. Gene Prediction with Conditional Random Fields. Tech Rep Technical Report UM-CS-2005-028 2005, University of Massachusetts, Amherst.
21. Bernal A, Crammer K, Hatzigeorgiou A, Pereira F. Global discriminative learning for higher-accuracy computational gene prediction. PLoS Comput Biol 2007, 3(3):e54. 10.1371/journal.pcbi.0030054, 1828702,1828702, 17367206.
22. Tompa M, Li N, Bailey TL, Church GM, De Moor B, Eskin E, Favorov AV, Frith MC, Fu Y, Kent WJ, Makeev VJ, Mironov AA, Noble WS, Pavesi G, Pesole G, Régnier M, Simonis N, Sinha S, Thijs G, van Helden J, Vandenbogaert M, Weng Z, Workman C, Ye C, Zhu Z. Assessing computational tools for the discovery of transcription factor binding sites. Nature Biotechnology 2005, 23:137-144. 10.1038/nbt1053, 15637633.
23. Ng AY, Jordan MI. On discriminative vs. generative classifiers: A comparison of logistic regression and naive bayes. Advances in Neural Information Processing Systems 2002, 14:605-610. Cambridge, MA: MIT Press, Dietterich T, Becker S, Ghahramani Z., http://citeseer.ist.psu.edu/542917.html
24. Greiner R, Su X, Shen B, Zhou W. Structural Extension to Logistic Regression: Discriminative Parameter Learning of Belief Net Classifiers. Machine Learning Journal 2005, 59(3):297-322.
25. Pernkopf F, Bilmes JA. Discriminative versus generative parameter and structure learning of Bayesian network classifiers. Proceedings of the 22nd International Conference on Machine Learning 2005, 657-664.
26. Grau J, Keilwagen J, Kel A, Grosse I, Posch S. Supervised posteriors for DNA-motif classification. German Conference on Bioinformatics, Lecture Notes in Informatics (LNI) - Proceedings 2007, 123-134. Gesellschaft für Informatik (GI), Falter C, Schliep A, Selbig J, Vingron M, Walter D.
27. Keilwagen J, Grau J, Posch S, Grosse I. Apples and oranges: avoiding different priors in Bayesian DNA sequence analysis. BMC Bioinformatics 2009,
28. Fisher RA. On the Mathematical Foundations of Theoretical Statistics. 1922,
29. Aldrich J. R. A. Fisher and the Making of Maximum Likelihood 1912-1922. Statistical Science 1997, 12(3):162-176.
30. Bishop CM. Pattern Recognition and Machine Learning 2006, Springer.
31. Redhead E, Bailey TL. Discriminative motif discovery in DNA and protein sequences using the DEME algorithm. BMC Bioinformatics 2007, 8:385. 10.1186/1471-2105-8-385, 2194741, 17937785.
32. Wettig H, Grünwald P, Roos T, Myllymäki P, Tirri H. On Supervised Learning of Bayesian Network Parameters. Tech Rep HIIT Technical Report 2002-1 2002, Helsinki Institute for Information Technology HIIT., http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.3.9589
33. Grossman D, Domingos P. Learning Bayesian network classifiers by maximizing conditional likelihood. ICML 2004, 361-368. ACM Press.
34. Feelders A, Ivanovs J. Discriminative Scoring of Bayesian Network Classifiers: a Comparative Study. Proceedings of the third European workshop on probabilistic graphical models 2006, 75-82.
35. Grüwald P, Kontkanen P, Myllymäki P, Roos T, Tirri H, Wettig H. Supervised posterior distributions. Presented at the Seventh Valencia International Meeting on Bayesian Statistics 2002,
36. Cerquides J, de Mántaras RL. Robust Bayesian Linear Classifier Ensembles. ECML 2005, 72-83.
37. Bouchard G, Triggs B. The Tradeoff Between Generative and Discriminative Classifiers. IASC International Symposium on Computational Statistics (COMPSTAT), Prague 2004, 721-728., http://lear.inrialpes.fr/pubs/2004/BT04
38. Raina R, Shen Y, Ng AY, McCallum A. Classification with Hybrid Generative/Discriminative Models. Advances in Neural Information Processing Systems 16 2004, Cambridge, MA: MIT Press, Thrun S, Saul L, Schölkopf B.
39. Lasserre JA, Bishop CM, Minka TP. Principled Hybrids of Generative and Discriminative Models. Proc IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2006, 1:87-94., http://research.microsoft.com/en-us/um/people/cmbishop/downloads/bishop-cvpr-06.pdf
40. Mccallum A, Pal C, Druck G, Wang X. Multi-conditional learning: Generative/discriminative training for clustering and classification. NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE 2006, 433-439., http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.67.5681
41. Bouchard G. Bias-Variance Tradeoff in Hybrid Generative-Discriminative Models. ICMLA '07: Proceedings of the Sixth International Conference on Machine Learning and Applications 2007, 124-129. Washington, DC, USA: IEEE Computer Society.
42. Xue JH, Titterington DM. Interpretation of hybrid generative/discriminative algorithms. Neurocomputing 2009, 72(7-9):1648-1655.
43. Hastie T, Tibshirani R, Friedman JH. The elements of statistical learning: data mining, inference, and prediction 2009, Springer., http://www-stat.stanford.edu/~hastie/Papers/ESLII.pdf
44. Wingender E, Dietze P, Karas H, Knuppel R. TRANSFAC: A database on transcription factors and their DNA binding sites. Nucleic Acids Res 1996, 24:238-241. 10.1093/nar/24.1.238, 145586, 8594589.
45. A Java framework for statistical analysis and classification of biological sequences. , http://www.jstacs.de/
46. Wallach H. Efficient Training of Conditional Random Fields. Master's thesis 2002, University of Edinburgh.
47. Jstacs Projects: GenDisMix. , http://www.jstacs.de/index.php/GenDisMix