Michal Nánási

Learn More
Hidden Markov models are traditionally decoded by the Viterbi algorithm which finds the highest probability state path in the model. In recent years, several limitations of the Viterbi decoding have been demonstrated, and new algorithms have been developed to address them (Kall et al. In this paper, we propose a new efficient highest expected reward(More)
Short tandem repeats are ubiquitous in genomic sequences and due to their complex evolutionary history pose a challenge for sequence alignment tools. To better account for the presence of tandem repeats in pairwise sequence alignments, we propose a simple tractable pair hidden Markov model that explicitly models their presence. Using the framework of gain(More)
Hidden Markov models (HMMs) and their variants were successfully used for several sequence annotation tasks. Traditionally, inference with HMMs is done using the Viterbi and posterior decoding algorithms. However, recently a variety of different optimization criteria and associated computational problems were proposed. In this paper, we consider three HMM(More)
Search for sequence similarity in large-scale databases of DNA and protein sequences is one of the essential problems in bioinformatics. To distinguish random matches from biologically relevant similarities, it is customary to compute statistical P-value of each discovered match. In this context, P-value is the probability that a similarity with a given(More)
  • 1