Predicting membrane protein types by incorporating protein topology, domains, signal peptides, and physicochemical properties into the general form of Chou's pseudo amino acid composition.
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iGPCR-drug: a web server for predicting interaction between GPCRs and drugs in cellular networkingiNitro-Tyr: prediction of nitrotyrosine sites in proteins with general pseudo amino acid compositionIdentification of real microRNA precursors with a pseudo structure status composition approachiEzy-drug: a web server for identifying the interaction between enzymes and drugs in cellular networkingiSNO-AAPair: incorporating amino acid pairwise coupling into PseAAC for predicting cysteine S-nitrosylation sites in proteinsiMethyl-PseAAC: identification of protein methylation sites via a pseudo amino acid composition approachiSS-PseDNC: identifying splicing sites using pseudo dinucleotide composition.iHyd-PseAAC: predicting hydroxyproline and hydroxylysine in proteins by incorporating dipeptide position-specific propensity into pseudo amino acid compositioniDNA-Prot|dis: identifying DNA-binding proteins by incorporating amino acid distance-pairs and reduced alphabet profile into the general pseudo amino acid compositioniPro54-PseKNC: a sequence-based predictor for identifying sigma-54 promoters in prokaryote with pseudo k-tuple nucleotide composition.Prediction of multi-type membrane proteins in human by an integrated approach.Exploring Mouse Protein Function via Multiple Approaches.Construction of the High-Density Genetic Linkage Map and Chromosome Map of Large Yellow Croaker (Larimichthys crocea).Protein Sub-Nuclear Localization Based on Effective Fusion Representations and Dimension Reduction Algorithm LDA.Naïve Bayes classifier with feature selection to identify phage virion proteinsPredicting Protein-Protein Interaction Sites Using Sequence Descriptors and Site Propensity of Neighboring Amino Acids.iRSpot-TNCPseAAC: identify recombination spots with trinucleotide composition and pseudo amino acid componentsPseAAC-General: fast building various modes of general form of Chou's pseudo-amino acid composition for large-scale protein datasets.iNR-Drug: predicting the interaction of drugs with nuclear receptors in cellular networking.Some remarks on predicting multi-label attributes in molecular biosystems.Pseudo nucleotide composition or PseKNC: an effective formulation for analyzing genomic sequences.A Prediction Model for Membrane Proteins Using Moments Based Features.Prediction of nucleosome positioning by the incorporation of frequencies and distributions of three different nucleotide segment lengths into a general pseudo k-tuple nucleotide composition.Prediction of Signal Peptide Cleavage Sites with Subsite-Coupled and Template Matching Fusion Algorithm.Protein Remote Homology Detection by Combining Chou's Pseudo Amino Acid Composition and Profile-Based Protein Representation.DSPMP: Discriminating secretory proteins of malaria parasite by hybridizing different descriptors of Chou's pseudo amino acid patterns.Amino acid composition analysis of secondary transport proteins from Escherichia coli with relation to functional classification, ligand specificity and structure.A multilabel model based on Chou's pseudo-amino acid composition for identifying membrane proteins with both single and multiple functional types.repRNA: a web server for generating various feature vectors of RNA sequences.Identification of DNA-binding proteins by incorporating evolutionary information into pseudo amino acid composition via the top-n-gram approach.iDNAProt-ES: Identification of DNA-binding Proteins Using Evolutionary and Structural Features.Combining evolutionary information extracted from frequency profiles with sequence-based kernels for protein remote homology detection.iLoc-Animal: a multi-label learning classifier for predicting subcellular localization of animal proteins.Protein remote homology detection by combining Chou's distance-pair pseudo amino acid composition and principal component analysis.Amino acid composition analysis of human secondary transport proteins and implications for reliable membrane topology prediction.An empirical study of different approaches for protein classification
P2860
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P2860
Predicting membrane protein types by incorporating protein topology, domains, signal peptides, and physicochemical properties into the general form of Chou's pseudo amino acid composition.
description
2012 nî lūn-bûn
@nan
2012 թուականի Նոյեմբերին հրատարակուած գիտական յօդուած
@hyw
2012 թվականի նոյեմբերին հրատարակված գիտական հոդված
@hy
2012年の論文
@ja
2012年論文
@yue
2012年論文
@zh-hant
2012年論文
@zh-hk
2012年論文
@zh-mo
2012年論文
@zh-tw
2012年论文
@wuu
name
Predicting membrane protein ty ...... pseudo amino acid composition.
@ast
Predicting membrane protein ty ...... pseudo amino acid composition.
@en
Predicting membrane protein ty ...... pseudo amino acid composition.
@nl
type
label
Predicting membrane protein ty ...... pseudo amino acid composition.
@ast
Predicting membrane protein ty ...... pseudo amino acid composition.
@en
Predicting membrane protein ty ...... pseudo amino acid composition.
@nl
prefLabel
Predicting membrane protein ty ...... pseudo amino acid composition.
@ast
Predicting membrane protein ty ...... pseudo amino acid composition.
@en
Predicting membrane protein ty ...... pseudo amino acid composition.
@nl
P1476
Predicting membrane protein ty ...... pseudo amino acid composition.
@en
P2093
Kuo-Bin Li
Yen-Kuang Chen
P356
10.1016/J.JTBI.2012.10.033
P407
P577
2012-11-05T00:00:00Z