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E-raamat: Protein Homology Detection Through Alignment of Markov Random Fields: Using MRFalign

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Protein Homology Detection Through Alignment of Markov Random Fields: Using MRFalignSuurem pilt
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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.

IntroductionMethodSoftwareExperiments and ResultsConclusion
1 Introduction
1(16)
1.1 Background
1(1)
1.2 Related Work
2(1)
1.3 Alignment-Free Methods for Homology Detection and Fold Recognition
2(3)
1.3.1 Generative and Discriminative Learning for Alignment-Free Homology Detection and Fold Recognition
4(1)
1.3.2 Kernel-Based Learning Methods for Alignment-Free Homology Detection
4(1)
1.4 Alignment-Based Methods for Homology Detection and Fold Recognition
5(7)
1.4.1 Sequence Alignment for Homology Detection and Fold Recognition
7(1)
1.4.2 Profile-Based Alignment for Homology Detection and Fold Recognition
8(1)
1.4.3 Scoring Function for Profile-Based Alignment and Homology Detection
9(1)
1.4.4 Scoring Function for Sequence-Profile Alignment and Comparison
10(1)
1.4.5 Scoring Function for Profile-Profile Alignment and Comparison
11(1)
1.5 Contribution of This Book
12(5)
References
13(4)
2 Method
17(14)
2.1 Modeling a Protein Family Using Markov Random Fields
17(1)
2.2 Estimating the Parameters of Markov Random Fields
18(3)
2.3 Scoring Similarity of Two Markov Random Fields
21(1)
2.4 Node Alignment Potential of Markov Random Fields
22(2)
2.5 Edge Alignment Potential of Markov Random Fields
24(2)
2.6 Scoring Similarity of One Markov Random Fields and One Template
26(1)
2.7 Algorithms for Aligning Two Markov Random Fields
26(5)
References
29(2)
3 Software
31(6)
3.1 Overview of Program
31(1)
3.2 Software Download
31(1)
3.3 Feature Files
32(1)
3.4 MRFsearch Ranking File
33(1)
3.5 Interpreting P-Value
34(1)
3.6 Interpreting a Pairwise Alignment
35(2)
References
36(1)
4 Experiments and Results
37(12)
4.1 Training and Validation Data
37(1)
4.2 Test Data
38(1)
4.3 Reference-Dependent Alignment Recall
39(2)
4.4 Reference-Dependent Alignment Precision
41(1)
4.5 Success Rate of Homology Detection and Fold Recognition
42(2)
4.6 Contribution of Edge Alignment Potential and Mutual Information
44(1)
4.7 Running Time
45(1)
4.8 Is Our MRFalign Method Overtrained?
45(4)
References
47(2)
Conclusion 49(2)
Acknowledgments 51
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