Conditional graphical models for protein structural motif recognition.
about
Markov random fields reveal an N-terminal double beta-propeller motif as part of a bacterial hybrid two-component sensor systemCharacterizing the regularity of tetrahedral packing motifs in protein tertiary structure.Recognition of beta-structural motifs using hidden Markov models trained with simulated evolutionSMURFLite: combining simplified Markov random fields with simulated evolution improves remote homology detection for beta-structural proteins into the twilight zone.Learning sequence determinants of protein:protein interaction specificity with sparse graphical models.
P2860
Conditional graphical models for protein structural motif recognition.
description
2009 nî lūn-bûn
@nan
2009 թուականի Մայիսին հրատարակուած գիտական յօդուած
@hyw
2009 թվականի մայիսին հրատարակված գիտական հոդված
@hy
2009年の論文
@ja
2009年論文
@yue
2009年論文
@zh-hant
2009年論文
@zh-hk
2009年論文
@zh-mo
2009年論文
@zh-tw
2009年论文
@wuu
name
Conditional graphical models for protein structural motif recognition.
@ast
Conditional graphical models for protein structural motif recognition.
@en
Conditional graphical models for protein structural motif recognition.
@nl
type
label
Conditional graphical models for protein structural motif recognition.
@ast
Conditional graphical models for protein structural motif recognition.
@en
Conditional graphical models for protein structural motif recognition.
@nl
prefLabel
Conditional graphical models for protein structural motif recognition.
@ast
Conditional graphical models for protein structural motif recognition.
@en
Conditional graphical models for protein structural motif recognition.
@nl
P2093
P356
P1476
Conditional graphical models for protein structural motif recognition.
@en
P2093
Peter Weigele
Vanathi Gopalakrishnan
P304
P356
10.1089/CMB.2008.0176
P577
2009-05-01T00:00:00Z