Better prediction of protein contact number using a support vector regression analysis of amino acid sequence.
about
Prodepth: predict residue depth by support vector regression approach from protein sequences onlyHighly accurate sequence-based prediction of half-sphere exposures of amino acid residues in proteinsHSEpred: predict half-sphere exposure from protein sequencesPrediction of cis/trans isomerization in proteins using PSI-BLAST profiles and secondary structure information.CRNPRED: highly accurate prediction of one-dimensional protein structures by large-scale critical random networks.Predicting residue-wise contact orders in proteins by support vector regressionProtein contact order prediction from primary sequences.AcconPred: Predicting Solvent Accessibility and Contact Number Simultaneously by a Multitask Learning Framework under the Conditional Neural Fields Model.iFC²: an integrated web-server for improved prediction of protein structural class, fold type, and secondary structure content.Capturing Non-Local Interactions by Long Short Term Memory Bidirectional Recurrent Neural Networks for Improving Prediction of Protein Secondary Structure, Backbone Angles, Contact Numbers, and Solvent Accessibility.Coarse- and fine-grained models for proteins: evaluation by decoy discrimination.STAR: predicting recombination sites from amino acid sequence.Prediction of flexible/rigid regions from protein sequences using k-spaced amino acid pairsNature of protein family signatures: insights from singular value analysis of position-specific scoring matrices.Sequence based residue depth prediction using evolutionary information and predicted secondary structureTANGLE: two-level support vector regression approach for protein backbone torsion angle prediction from primary sequencesTowards more accurate prediction of protein folding rates: a review of the existing Web-based bioinformatics approaches.Accurate Prediction of Contact Numbers for Multi-Spanning Helical Membrane Proteins.Amino acid composition and protein dimension.RRCRank: a fusion method using rank strategy for residue-residue contact predictionA novel topology for representing protein folds.Characterization of non-trivial neighborhood fold constraints from protein sequences using generalized topohydrophobicity.Cascleave: towards more accurate prediction of caspase substrate cleavage sites.3DCONS-DB: A Database of Position-Specific Scoring Matrices in Protein Structures.Predicting disulfide connectivity from protein sequence using multiple sequence feature vectors and secondary structure.A sparse autoencoder-based deep neural network for protein solvent accessibility and contact number prediction.
P2860
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P2860
Better prediction of protein contact number using a support vector regression analysis of amino acid sequence.
description
2005 nî lūn-bûn
@nan
2005 թուականի Հոկտեմբերին հրատարակուած գիտական յօդուած
@hyw
2005 թվականի հոտեմբերին հրատարակված գիտական հոդված
@hy
2005年の論文
@ja
2005年論文
@yue
2005年論文
@zh-hant
2005年論文
@zh-hk
2005年論文
@zh-mo
2005年論文
@zh-tw
2005年论文
@wuu
name
Better prediction of protein c ...... nalysis of amino acid sequence
@nl
Better prediction of protein c ...... alysis of amino acid sequence.
@ast
Better prediction of protein c ...... alysis of amino acid sequence.
@en
type
label
Better prediction of protein c ...... nalysis of amino acid sequence
@nl
Better prediction of protein c ...... alysis of amino acid sequence.
@ast
Better prediction of protein c ...... alysis of amino acid sequence.
@en
prefLabel
Better prediction of protein c ...... nalysis of amino acid sequence
@nl
Better prediction of protein c ...... alysis of amino acid sequence.
@ast
Better prediction of protein c ...... alysis of amino acid sequence.
@en
P2860
P356
P1433
P1476
Better prediction of protein c ...... alysis of amino acid sequence.
@en
P2093
Zheng Yuan
P2860
P2888
P356
10.1186/1471-2105-6-248
P407
P577
2005-10-13T00:00:00Z
P5875
P6179
1018449238