Accurate prediction of protein secondary structure and solvent accessibility by consensus combiners of sequence and structure information.
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Impact of residue accessible surface area on the prediction of protein secondary structuresPROTEUS2: a web server for comprehensive protein structure prediction and structure-based annotationThe Jpred 3 secondary structure prediction serverPrediction of protein secondary structure using probability based features and a hybrid system.Toward an accurate prediction of inter-residue distances in proteins using 2D recursive neural networks.Template-based C8-SCORPION: a protein 8-state secondary structure prediction method using structural information and context-based featuresAccurate single-sequence prediction of solvent accessible surface area using local and global featuresProtein structure search and local structure characterizationLong-range information and physicality constraints improve predicted protein contact maps.Ab initio and template-based prediction of multi-class distance maps by two-dimensional recursive neural networksTemplate-based protein modeling: recent methodological advances.A simple graphical approach to predict local residue conformation using NMR chemical shifts and density functional theory.Bioinformatic Analysis of the Human Recombinant Iduronate 2-Sulfate Sulfatase.Protein secondary structure prediction using a small training set (compact model) combined with a Complex-valued neural network approach.Fast and Accurate Accessible Surface Area Prediction Without a Sequence Profile.Sixty-five years of the long march in protein secondary structure prediction: the final stretch?Accurate prediction of protein relative solvent accessibility using a balanced model.LocateP: genome-scale subcellular-location predictor for bacterial proteins.Ab initio and homology based prediction of protein domains by recursive neural networksA generic method for assignment of reliability scores applied to solvent accessibility predictions.SUMOylation of the lens epithelium-derived growth factor/p75 attenuates its transcriptional activity on the heat shock protein 27 promoter.Distributions of amino acids suggest that certain residue types more effectively determine protein secondary structure.CONS-COCOMAPS: a novel tool to measure and visualize the conservation of inter-residue contacts in multiple docking solutionsThe plant apoplasm is an important recipient compartment for nematode secreted proteins.Accurate prediction of protein enzymatic class by N-to-1 Neural Networks.SPINE X: improving protein secondary structure prediction by multistep learning coupled with prediction of solvent accessible surface area and backbone torsion anglesSurface masking shapes the traffic of the neuropeptide Y Y2 receptor.Fast learning optimized prediction methodology (FLOPRED) for protein secondary structure predictionCell cycle kinases predicted from conserved biophysical propertiesOn the segregation of protein ionic residues by charge type.Predicting protein submitochondria locations by combining different descriptors into the general form of Chou's pseudo amino acid composition.Implication of serine residues 271, 273, and 275 in the human immunodeficiency virus type 1 cofactor activity of lens epithelium-derived growth factor/p75.Reconstructing protein structures by neural network pairwise interaction fields and iterative decoy set construction.Accurate Ab Initio and Template-Based Prediction of Short Intrinsically-Disordered Regions by Bidirectional Recurrent Neural Networks Trained on Large-Scale Datasets.CSpritz: accurate prediction of protein disorder segments with annotation for homology, secondary structure and linear motifs.SCLpredT: Ab initio and homology-based prediction of subcellular localization by N-to-1 neural networks.Prediction of delayed retention of antibodies in hydrophobic interaction chromatography from sequence using machine learning.Exploring the ligand recognition properties of the human vasopressin V1a receptor using QSAR and molecular modeling studies.acACS: improving the prediction accuracy of protein subcellular locations and protein classification by incorporating the average chemical shifts composition.Prediction of One-Dimensional Structural Properties Of Proteins by Integrated Neural Networks
P2860
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P2860
Accurate prediction of protein secondary structure and solvent accessibility by consensus combiners of sequence and structure information.
description
2007 nî lūn-bûn
@nan
2007 թուականի Յունիսին հրատարակուած գիտական յօդուած
@hyw
2007 թվականի հունիսին հրատարակված գիտական հոդված
@hy
2007年の論文
@ja
2007年論文
@yue
2007年論文
@zh-hant
2007年論文
@zh-hk
2007年論文
@zh-mo
2007年論文
@zh-tw
2007年论文
@wuu
name
Accurate prediction of protein ...... nce and structure information.
@ast
Accurate prediction of protein ...... nce and structure information.
@en
Accurate prediction of protein ...... nce and structure information.
@nl
type
label
Accurate prediction of protein ...... nce and structure information.
@ast
Accurate prediction of protein ...... nce and structure information.
@en
Accurate prediction of protein ...... nce and structure information.
@nl
prefLabel
Accurate prediction of protein ...... nce and structure information.
@ast
Accurate prediction of protein ...... nce and structure information.
@en
Accurate prediction of protein ...... nce and structure information.
@nl
P2093
P2860
P356
P1433
P1476
Accurate prediction of protein ...... nce and structure information.
@en
P2093
Alberto J M Martin
Catherine Mooney
Gianluca Pollastri
P2860
P2888
P356
10.1186/1471-2105-8-201
P577
2007-06-14T00:00:00Z
P5875
P6179
1052139506