Prediction of protein structural class using novel evolutionary collocation-based sequence representation.
about
Plant-mPLoc: a top-down strategy to augment the power for predicting plant protein subcellular localizationPrediction of ubiquitination sites by using the composition of k-spaced amino acid pairsA new method for predicting the subcellular localization of eukaryotic proteins with both single and multiple sites: Euk-mPLoc 2.0A multi-label classifier for predicting the subcellular localization of gram-negative bacterial proteins with both single and multiple sitesSUMOhydro: a novel method for the prediction of sumoylation sites based on hydrophobic propertiesPSSP-RFE: accurate prediction of protein structural class by recursive feature extraction from PSI-BLAST profile, physical-chemical property and functional annotationsProposing a highly accurate protein structural class predictor using segmentation-based features.Customised fragments libraries for protein structure prediction based on structural class annotations.iFC²: an integrated web-server for improved prediction of protein structural class, fold type, and secondary structure content.Using principal component analysis and support vector machine to predict protein structural class for low-similarity sequences via PSSM.Exploring protein structural dissimilarity to facilitate structure classification.Modular prediction of protein structural classes from sequences of twilight-zone identity with predicting sequences.Prediction of protein structural classes for low-homology sequences based on predicted secondary structure.Prediction and analysis of protein solubility using a novel scoring card method with dipeptide compositionUsing weakly conserved motifs hidden in secretion signals to identify type-III effectors from bacterial pathogen genomes.Comparison study on statistical features of predicted secondary structures for protein structural class prediction: From content to position.Predicting the molecular interactions of CRIP1a-cannabinoid 1 receptor with integrated molecular modeling approachesGlycoMine: a machine learning-based approach for predicting N-, C- and O-linked glycosylation in the human proteome.BEAN 2.0: an integrated web resource for the identification and functional analysis of type III secreted effectors.Prediction of Protein Structural Classes for Low-Similarity Sequences Based on Consensus Sequence and Segmented PSSM.Prediction of Protein Structural Class Based on Gapped-Dipeptides and a Recursive Feature Selection Approach.Statistical prediction of protein structural, localization and functional properties by the analysis of its fragment mass distributions after proteolytic cleavage.Deletion of Murid Herpesvirus 4 ORF63 Affects the Trafficking of Incoming Capsids toward the NucleusSOHPRED: a new bioinformatics tool for the characterization and prediction of human S-sulfenylation sites.SuccinSite: a computational tool for the prediction of protein succinylation sites by exploiting the amino acid patterns and properties.Structural classification of proteins using texture descriptors extracted from the cellular automata image.Novel numerical characterization of protein sequences based on individual amino acid and its application.Efficient Biosynthesis of Fungal Polyketides Containing the Dioxabicyclo-octane Ring SystemAccurate prediction of protein structural classes using functional domains and predicted secondary structure sequences.The prediction of protein structural class using averaged chemical shifts.Learning protein multi-view features in complex space.ZincExplorer: an accurate hybrid method to improve the prediction of zinc-binding sites from protein sequences.A novel graphical representation of protein sequences and its application.CIPPN: computational identification of protein pupylation sites by using neural network.A novel Multi-Agent Ada-Boost algorithm for predicting protein structural class with the information of protein secondary structure.Prediction of protein structural classes based on feature selection technique.Accurate prediction of protein structural class using auto covariance transformation of PSI-BLAST profiles.
P2860
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P2860
Prediction of protein structural class using novel evolutionary collocation-based sequence representation.
description
2008 nî lūn-bûn
@nan
2008年の論文
@ja
2008年学术文章
@wuu
2008年学术文章
@zh
2008年学术文章
@zh-cn
2008年学术文章
@zh-hans
2008年学术文章
@zh-my
2008年学术文章
@zh-sg
2008年學術文章
@yue
2008年學術文章
@zh-hant
name
Prediction of protein structur ...... based sequence representation.
@en
Prediction of protein structur ...... based sequence representation.
@nl
type
label
Prediction of protein structur ...... based sequence representation.
@en
Prediction of protein structur ...... based sequence representation.
@nl
prefLabel
Prediction of protein structur ...... based sequence representation.
@en
Prediction of protein structur ...... based sequence representation.
@nl
P356
P1476
Prediction of protein structur ...... based sequence representation.
@en
P2093
P304
P356
10.1002/JCC.20918
P577
2008-07-01T00:00:00Z