SPINE X: improving protein secondary structure prediction by multistep learning coupled with prediction of solvent accessible surface area and backbone torsion angles
about
SPINE-D: accurate prediction of short and long disordered regions by a single neural-network based methodImproving protein fold recognition and template-based modeling by employing probabilistic-based matching between predicted one-dimensional structural properties of query and corresponding native properties of templatesDisPredict: A Predictor of Disordered Protein Using Optimized RBF KernelPSIONplus: Accurate Sequence-Based Predictor of Ion Channels and Their TypesThe Parkinson Disease gene SNCA: Evolutionary and structural insights with pathological implicationFastRNABindR: Fast and Accurate Prediction of Protein-RNA Interface ResiduesA novel fatty acid-binding protein-like carotenoid-binding protein from the gonad of the New Zealand sea urchin Evechinus chloroticus.SSThread: Template-free protein structure prediction by threading pairs of contacting secondary structures followed by assembly of overlapping pairs.Proposing a highly accurate protein structural class predictor using segmentation-based features.Direct prediction of profiles of sequences compatible with a protein structure by neural networks with fragment-based local and energy-based nonlocal profilesAccurate single-sequence prediction of solvent accessible surface area using local and global featuresBuilding a better fragment library for de novo protein structure prediction.CONFOLD: Residue-residue contact-guided ab initio protein folding.Large-scale model quality assessment for improving protein tertiary structure prediction.Improving prediction of secondary structure, local backbone angles, and solvent accessible surface area of proteins by iterative deep learning.Effective protein conformational sampling based on predicted torsion angles.Protein Secondary Structure Prediction Using Deep Convolutional Neural FieldsProtein single-model quality assessment by feature-based probability density functions.RaptorX-Property: a web server for protein structure property prediction.Estimation of Position Specific Energy as a Feature of Protein Residues from Sequence Alone for Structural ClassificationPredicting Protein Secondary Structure Using Consensus Data Mining (CDM) Based on Empirical Statistics and Evolutionary Information.Accurate Prediction of One-Dimensional Protein Structure Features Using SPINE-X.Fast and Accurate Accessible Surface Area Prediction Without a Sequence Profile.An Evolution-Based Approach to De Novo Protein Design.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.Deep learning for computational chemistry.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.DDIG-in: discriminating between disease-associated and neutral non-frameshifting micro-indelsTowards sequence-based prediction of mutation-induced stability changes in unseen non-homologous proteins.Feature-based multiple models improve classification of mutation-induced stability changes.Accurate in silico identification of species-specific acetylation sites by integrating protein sequence-derived and functional features.Distributions of amino acids suggest that certain residue types more effectively determine protein secondary structure.Testing whether metazoan tyrosine loss was driven by selection against promiscuous phosphorylation.Improving protein fold recognition using the amalgamation of evolutionary-based and structural based information.An evolution-based approach to De Novo protein design and case study on Mycobacterium tuberculosis.PAIRpred: partner-specific prediction of interacting residues from sequence and structureA Deep Learning Network Approach to ab initio Protein Secondary Structure Prediction.Predicting the molecular interactions of CRIP1a-cannabinoid 1 receptor with integrated molecular modeling approachesEvaluation of protein dihedral angle prediction methods.
P2860
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P2860
SPINE X: improving protein secondary structure prediction by multistep learning coupled with prediction of solvent accessible surface area and backbone torsion angles
description
2011 nî lūn-bûn
@nan
2011年の論文
@ja
2011年論文
@yue
2011年論文
@zh-hant
2011年論文
@zh-hk
2011年論文
@zh-mo
2011年論文
@zh-tw
2011年论文
@wuu
2011年论文
@zh
2011年论文
@zh-cn
name
SPINE X: improving protein sec ...... ea and backbone torsion angles
@ast
SPINE X: improving protein sec ...... ea and backbone torsion angles
@en
type
label
SPINE X: improving protein sec ...... ea and backbone torsion angles
@ast
SPINE X: improving protein sec ...... ea and backbone torsion angles
@en
prefLabel
SPINE X: improving protein sec ...... ea and backbone torsion angles
@ast
SPINE X: improving protein sec ...... ea and backbone torsion angles
@en
P2860
P50
P356
P1476
SPINE X: improving protein sec ...... ea and backbone torsion angles
@en
P2093
Eshel Faraggi
P2860
P304
P356
10.1002/JCC.21968
P577
2011-11-02T00:00:00Z