Protein Homology Detection Through Alignment of Markov Random Fields : Using MRFalign / by Jinbo Xu, Sheng Wang, Jianzhu Ma.

By:

Xu, Jinbo [author.]

Contributor(s):

Material type: Text

TextLanguage: İngilizce Series: SpringerBriefs in Computer SciencePublisher: Cham : Springer International Publishing : Imprint: Springer, 2015Description: 1 online resourceContent type:

text

Media type:

computer

Carrier type:

online resource

ISBN:

9783319149141

Subject(s):

LOC classification:

QH324.2-324.25

Online resources:

Springer eBooks Online access link to the resource

Contents:

Introduction -- Method -- Software -- Experiments and Results -- Conclusion.

Summary: 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.

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Holdings
Item type	Current library	Home library	Collection	Call number	Status	Notes	Date due	Barcode
E-Book	Merkez Kütüphane	Merkez Kütüphane	E-Kitap Koleksiyonu	QH324.2-324.25EBK (Browse shelf(Opens below))	Geçerli değil-e-Kitap / Not applicable-e-Book	BİL		EBK00783

Introduction -- Method -- Software -- Experiments and Results -- Conclusion.

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.

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