© 2015

Protein Homology Detection Through Alignment of Markov Random Fields

Using MRFalign

  • Surveys the key topics to aid the reader in quickly learning about this area

  • Presents a novel technique for protein homology search, preparing the reader for future developments

  • Provides an introduction to the software and the web server, enabling the reader to easily make use of the new technique


Part of the SpringerBriefs in Computer Science book series (BRIEFSCOMPUTER)

Table of contents

  1. Front Matter
    Pages i-viii
  2. Jinbo Xu, Sheng Wang, Jianzhu Ma
    Pages 1-16
  3. Jinbo Xu, Sheng Wang, Jianzhu Ma
    Pages 17-30
  4. Jinbo Xu, Sheng Wang, Jianzhu Ma
    Pages 31-36
  5. Jinbo Xu, Sheng Wang, Jianzhu Ma
    Pages 37-48
  6. Back Matter
    Pages 49-51

About this book


This work covers sequence-based protein homology detection, a fundamental and challenging bioinformatics problem with a variety of real-world applications. The text first surveys a few popular homology detection methods, such as Position-Specific Scoring Matrix (PSSM) and Hidden Markov Model (HMM) based methods, and then describes a novel Markov Random Fields (MRF) based method developed by the authors. MRF-based methods are much more sensitive than HMM- and PSSM-based methods for remote homolog detection and fold recognition, as MRFs can model long-range residue-residue interaction. The text also describes the installation, usage and result interpretation of programs implementing the MRF-based method.


Hidden Markov Model Long-Range Residue Interaction Markov Random Fields Position-Specific Scoring Matrix Protein Homology Detection

Authors and affiliations

  1. 1.Toyota Technological InstituteChicagoUSA
  2. 2.Toyota Technological InstituteChicagoUSA
  3. 3.Toyota Technological InstituteChicagoUSA

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