© 2015

Protein Homology Detection Through Alignment of Markov Random Fields

Using MRFalign


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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