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Πλοήγηση ανά Θέμα "hidden Markov model"

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    • Hidden neural networks for transmembrane protein topology prediction 

      Tamposis I.A., Sarantopoulou D., Theodoropoulou M.C., Stasi E.A., Kontou P.I., Tsirigos K.D., Bagos P.G. (2021)
      Hidden Markov Models (HMMs) are amongst the most successful methods for predicting protein features in biological sequence analysis. However, there are biological problems where the Markovian assumption is not sufficient ...

    • JUCHMME: A Java Utility for Class Hidden Markov Models and Extensions for biological sequence analysis 

      Tamposis I.A., Tsirigos K.D., Theodoropoulou M.C., Kontou P.I., Tsaousis G.N., Sarantopoulou D., Litou Z.I., Bagos P.G. (2019)
      JUCHMME is an open-source software package designed to fit arbitrary custom Hidden Markov Models (HMMs) with a discrete alphabet of symbols. We incorporate a large collection of standard algorithms for HMMs as well as a ...

    • Landscape of Eukaryotic Transmembrane Beta Barrel Proteins 

      Roumia A.F., Theodoropoulou M.C., Tsirigos K.D., Nielsen H., Bagos P.G. (2020)
      Even though in the last few years several families of eukaryotic β-barrel outer membrane proteins have been discovered, their computational characterization and their annotation in public databases are far from complete. ...

    • Predicting alpha helical transmembrane proteins using HMMs 

      Tsaousis G.N., Theodoropoulou M.C., Hamodrakas S.J., Bagos P.G. (2017)
      Alpha helical transmembrane (TM) proteins constitute an important structural class of membrane proteins involved in a wide variety of cellular functions. The prediction of their transmembrane topology, as well as their ...

    • Predicting beta barrel transmembrane proteins using HMMs 

      Tsaousis G.N., Hamodrakas S.J., Bagos P.G. (2017)
      Transmembrane beta-barrels (TMBBs) constitute an important structural class of membrane proteins located in the outer membrane of gram-negative bacteria, and in the outer membrane of chloroplasts and mitochondria. They are ...