Journal of Proteomics & Bioinformatics
Open Access

Sima Naghizadeh

Publications

• Research Article
  A Modified Hidden Markov Model and Its Application in Protein Secondary Structure Prediction
  Author(s): Sima Naghizadeh, Vahid Rezaeitabar, Hamid Pezeshk and David MatthewsSima Naghizadeh, Vahid Rezaeitabar, Hamid Pezeshk and David Matthews
  
  One of the important tools in analyzing and modeling biological data is the Hidden Markov Model (HMM), which is used for gene prediction, protein secondary structure and other essential tasks. An HMM is a stochastic process in which a hidden Markov chain called; the chain of states, emits a sequence of observations. Using this sequence, various questions about the underlying emission generation scheme can be addressed. Applying an HMM to any particular situation is an attempt to infer which state in the chain emits an observation. This is usually called posterior decoding. In general, the emissions are assumed to be conditionally independent from each other. In this work we consider some dependencies among the states and emissions. The aim of our research is to study a certain relationship among emissions, with a focus on the Markov property. We assume that the probability of observin.. View More»
  DOI: 10.4172/jpb.1000209

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