A graphical model formulation of the DNA base-calling problem

2005 IEEE Workshop on Machine Learning for Signal Processing

Volumn , Issue , 2005, Pages 369-374

  Andrade Cetto, Lucio ^a   Manolakos, Elias S ^a  

^a Northeastern University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

FIRST ORDER VARIABLE DEPENDENCE (FOVD); SIGNAL PEAKS; UNSUPERVISED CLASSIFICATION;

CLASSIFICATION (OF INFORMATION); COSTS; DNA SEQUENCES; GRAPH THEORY; LEARNING SYSTEMS; MATHEMATICAL MODELS; MAXIMUM LIKELIHOOD ESTIMATION; PARAMETER ESTIMATION;

PROBLEM SOLVING;

EID: 33749065549     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/MLSP.2005.1532931     Document Type: Conference Paper

References (16)

1. M. Pereira, L. Andrade, S. El-Difrawy, B. Karger, and E. Manolakos, "Statistical learning formulation of the DNA base-calling problem and its solution using a Bayesian EM framework," Discrete Applied Mathematics, vol. 104, no. 1-3, pp. 229-258, 2000.
2. L. Andrade and E. Manolakos, "Automatic Estimation of Mobility Shift Coefficients in DNA chromatograms," in Proceedings of the IEEE Workshop on Neural Networks for Signal Processing (NNSP), Sept. 2003, pp. 91-100.
3. L. Andrade and E. Manolakos, "Signal Background Estimation and Baseline Correction Algorithms for Accurate DNA Sequencing," The Journal of VLSI Signal Processing-Systems for Signal, Image, and Video Technology, vol. 35, no. 3, pp. 229-243, Nov. 2003, special Issue on Signal Processing and Neural Networks for Bioinformatics.
4. L. Andrade and E. Manolakos, "Robust Normalization of DNA Chromatograms by Regression for improved Base-calling," Journal of the Franklin Institute, vol. 341, no. 1-2, pp. 3-22, 2004, special Issue on Genomic Signal Processing.
5. L. Andrade-Cetto and E. Manolakos, "Feature extraction for DNA base-calling using NNLS," in Proceedings of the IEEE Workshop on Statistical Signal Processing, Jul. 2005.
6. L.R. Rabiner, "A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition," Proceedings of the IEEE, vol. 77, no. 2, pp. 267-296, Feb 1989.
7. D. Heckerman, "A tutorial on learning with bayesian networks," Tech. Rep. MST-TR-95-06, Microsoft Research, Nov 1996.
8. B.J. Frey, Graphical Models for Machine Learning and Digital Communications, MIT Press, 1998.
9. B.D. Ripley, Stochastic Simulation, JohnWiley, 1987.
10. T. Jaakkola and M.I. Jordan, "A variational approach to Bayesian logistic regression models and their extensions," in Sixth International Workshop on Artificial Intelligence and Statistics, 1997.
11. P. Dayan, G.E. Hinton, R.M. Neal, and R.S. Zemel, "The helmholtz machine," Neural Computation, vol. 7, pp. 565-579, 1995.
12. G.E. Hinton, P. Dayan, B.J. Frey, and R.M. Neal, "The wake-sleep algorithm for unsupervised neural networks," Science, vol. 268, pp. 1158-1161, 1995.
13. International Human Genome Sequencing Consortium, "Initial sequencing and analysis of the human genome," Nature, vol. 409, pp. 860-920, February 2001.
14. L. Andrade-Cetto and E. Manolakos, "Inter-peak distance equalization for DNA sequencing," 2005, in preparation.
15. B. Ewing, L. Hillier, M. Wendl, and P. Green, "Base-calling of automated sequencer traces using phred. I. Accuracy assessment," Genome Research, vol. 8, pp. 175-185, 1998.
16. L. Andrade-Cetto, Analysis of DNA Chromatograms using Unsupervised Statistical Learning Methods., ECE Department, Northeastern University, 2005, Ph.D. dissertation in preparation.