Statistical learning formulation of the DNA base-calling problem and its solution in a Bayesian EM framework

This publication has 11 references indexed in Scilit:

• A Software System for Data Analysis in Automated DNA Sequencing
  Genome Research, 1998
• Base-Calling of Automated Sequencer Traces UsingPhred. I. Accuracy Assessment
  Genome Research, 1998
• Unsupervised statistical neural networks for model-based object recognition
  IEEE Transactions on Signal Processing, 1997
• A graph theoretic approach to the analysis of DNA sequencing data.
  Genome Research, 1996
• Maximum likelihood training of probabilistic neural networks
  IEEE Transactions on Neural Networks, 1994
• An adaptive, object oriented strategy for base calling in DNA sequence analysis
  Nucleic Acids Research, 1993
• Maximum-likelihood localization of narrow-band autoregressive sources via the EM algorithm
  IEEE Transactions on Signal Processing, 1993
• Application of expectation-maximization algorithm to the detection of a direct-sequence signal in pulsed noise jamming
  IEEE Transactions on Communications, 1993
• An expectation maximization (EM) algorithm for the identification and characterization of common sites in unaligned biopolymer sequences
  Proteins-Structure Function and Bioinformatics, 1990
• A general method applicable to the search for similarities in the amino acid sequence of two proteins
  Journal of Molecular Biology, 1970