TY  - GEN
AB  - 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.
AU  - Xu, Jinbo,
AU  - Wang, Sheng,
AU  - Ma, Jianzhu,
CN  - QA274.45
DO  - 10.1007/978-3-319-14914-1
DO  - doi
ID  - 725340
KW  - Markov random fields.
KW  - Sequence alignment (Bioinformatics)
KW  - Bioinformatics.
LK  - https://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-319-14914-1
N2  - 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.
SN  - 9783319149141
SN  - 3319149148
T1  - Protein homology detection through alignment of Markov random fieldsusing MRFalign /
TI  - Protein homology detection through alignment of Markov random fieldsusing MRFalign /
UR  - https://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-319-14914-1
ER  -