Problems and Solutions in Biological Sequence Analysis

By Mark Borodovsky, S. Ekisheva. 2006

621 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/c818d81db54329f0a7d71bc2648b71311670718b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

Citations in the corpus

Graphical Models, Exponential Families, and Variational Inference (2008, methodology): “Gene-finding provides an important example of the application of the HMM [69].”
A Monte Carlo approach successfully identifies randomness in multiple sequence alignments: a more objective means of data exclusion. (2009, background): “Highly diverse, but related sequences plagued by mutational saturation and variable sequence length are often only ambiguously alignable because different alignment solutions can lead to almost identical similarity scores indistinguishable from similarity scores of randomly drawn sequences (Gatesy e”
Score-based prediction of genomic islands in prokaryotic genomes using hidden Markov models (2006, methodology): “Using the Viterbi algorithm (see e.g. [33,34]), SIGI-HMM computes at first the Viterbi path (i.e. the most probable sequence of states).”
Selective Markov models for predicting Web page accesses (2004, methodology): “In such scenarios, a standard technique to obtain nonzero conditional probabilities for such states is known as smoothing [Durbin et al. 1998], which adds a small nonzero number to the frequencies of all the states.”
ProbCons: Probabilistic consistency-based multiple sequence alignment. (2005, methodology): “The ProbCons algorithm begins by calculating these posterior probability matrices using a modification of the Forward and Backward algorithms for computing posterior probabilities in pair-HMMs as described in Durbin et al. (1998). This computation step takes time O(mL), where m is the number of sequ”