國立虎尾科技大學 |

Protein Homology Detection Through Alignment of Markov Random Fields = Using MRFalign /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Protein Homology Detection Through Alignment of Markov Random Fields/ by Jinbo Xu, Sheng Wang, Jianzhu Ma.
Reminder of title:	Using MRFalign /
Author:	Xu, Jinbo.
other author:	Wang, Sheng.
Description:	VIII, 51 p. 13 illus., 1 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Bioinformatics. -
Online resource:	https://doi.org/10.1007/978-3-319-14914-1
ISBN:	9783319149141

Protein Homology Detection Through Alignment of Markov Random Fields = Using MRFalign /
Xu, Jinbo.

Protein Homology Detection Through Alignment of Markov Random FieldsUsing MRFalign /[electronic resource] :by Jinbo Xu, Sheng Wang, Jianzhu Ma. - 1st ed. 2015. - VIII, 51 p. 13 illus., 1 illus. in color.online resource. - SpringerBriefs in Computer Science,2191-5768. - SpringerBriefs in Computer Science,.

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.

ISBN: 9783319149141

Standard No.: 10.1007/978-3-319-14914-1doiSubjects--Topical Terms:

583857
Bioinformatics.

LC Class. No.: QH324.2-324.25

Dewey Class. No.: 570.285

Protein Homology Detection Through Alignment of Markov Random Fields = Using MRFalign /
LDR:02226nam a22004095i 4500 001 963154
003 DE-He213
005 20200706071946.0
007 cr nn 008mamaa
008 201211s2015 gw | s |||| 0|eng d
020 $a 9783319149141 $9 978-3-319-14914-1
024 7 $a 10.1007/978-3-319-14914-1 $2 doi
035 $a 978-3-319-14914-1
050 4 $a QH324.2-324.25
072 7 $a PSA $2 bicssc
072 7 $a COM014000 $2 bisacsh
072 7 $a PSA $2 thema
072 7 $a UB $2 thema
082 0 4 $a 570.285 $2 23
100 1 $a Xu, Jinbo. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1065655
245 1 0 $a Protein Homology Detection Through Alignment of Markov Random Fields $h [electronic resource] : $b Using MRFalign / $c by Jinbo Xu, Sheng Wang, Jianzhu Ma.
250 $a 1st ed. 2015.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2015.
300 $a VIII, 51 p. 13 illus., 1 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
490 1 $a SpringerBriefs in Computer Science, $x 2191-5768
505 0 $a Introduction -- Method -- Software -- Experiments and Results -- Conclusion.
520 $a 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.
650 0 $a Bioinformatics. $3 583857
650 0 $a Mathematical statistics. $3 527941
650 0 $a Statistics . $3 1253516
650 1 4 $a Computational Biology/Bioinformatics. $3 677363
650 2 4 $a Probability and Statistics in Computer Science. $3 669886
650 2 4 $a Statistics for Life Sciences, Medicine, Health Sciences. $3 670172
700 1 $a Wang, Sheng. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1065656
700 1 $a Ma, Jianzhu. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1065657
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783319149134
776 0 8 $i Printed edition: $z 9783319149158
830 0 $a SpringerBriefs in Computer Science, $x 2191-5768 $3 1255334
856 4 0 $u https://doi.org/10.1007/978-3-319-14914-1
912 $a ZDB-2-SCS
912 $a ZDB-2-SXCS
950 $a Computer Science (SpringerNature-11645)
950 $a Computer Science (R0) (SpringerNature-43710)