Prediction of G-protein-coupled receptor classes in low homology using Chou's pseudo amino acid composition with approximate entropy and hydrophobicity patterns.
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Plant-mPLoc: a top-down strategy to augment the power for predicting plant protein subcellular localizationA survey of computational intelligence techniques in protein function predictionPrediction of body fluids where proteins are secreted into based on protein interaction networkPredicting transcriptional activity of multiple site p53 mutants based on hybrid propertiesClassification and analysis of regulatory pathways using graph property, biochemical and physicochemical property, and functional propertyPrediction of antimicrobial peptides based on sequence alignment and feature selection methodsPrediction of protein domain with mRMR feature selection and analysisiNR-PhysChem: a sequence-based predictor for identifying nuclear receptors and their subfamilies via physical-chemical property matrixPredicting Anatomical Therapeutic Chemical (ATC) classification of drugs by integrating chemical-chemical interactions and similaritiesA multi-label predictor for identifying the subcellular locations of singleplex and multiplex eukaryotic proteinsIn vitro transcriptomic prediction of hepatotoxicity for early drug discoveryGene ontology based transfer learning for protein subcellular localizationiDNA-Prot: identification of DNA binding proteins using random forest with grey model.Predicting protein folding rates using the concept of Chou's pseudo amino acid composition.Quantitative analysis of cellular metabolic dissipative, self-organized structuresIdentification of colorectal cancer related genes with mRMR and shortest path in protein-protein interaction network.Prediction of P53 mutants (multiple sites) transcriptional activity based on structural (2D&3D) properties.3D QSAR pharmacophore modeling, in silico screening, and density functional theory (DFT) approaches for identification of human chymase inhibitors.An Alignment-Free Algorithm in Comparing the Similarity of Protein Sequences Based on Pseudo-Markov Transition Probabilities among Amino Acids.PseAAC-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.A Method for the Annotation of Functional Similarities of Coding DNA Sequences: the Case of a Populated Cluster of Transmembrane Proteins.Comprehensive comparative analysis and identification of RNA-binding protein domains: multi-class classification and feature selection.Predicting protein submitochondria locations by combining different descriptors into the general form of Chou's pseudo amino acid composition.Pseudo nucleotide composition or PseKNC: an effective formulation for analyzing genomic sequences.iRSpot-GAEnsC: identifing recombination spots via ensemble classifier and extending the concept of Chou's PseAAC to formulate DNA samples.Amino acid composition analysis of secondary transport proteins from Escherichia coli with relation to functional classification, ligand specificity and structure.PseDNA-Pro: DNA-Binding Protein Identification by Combining Chou's PseAAC and Physicochemical Distance Transformation.Analysis on folding of misgurin using two-dimensional HP model.Prediction of ketoacyl synthase family using reduced amino acid alphabets.EuLoc: a web-server for accurately predict protein subcellular localization in eukaryotes by incorporating various features of sequence segments into the general form of Chou's PseAAC.GPCR-2L: predicting G protein-coupled receptors and their types by hybridizing two different modes of pseudo amino acid compositions.LIBP-Pred: web server for lipid binding proteins using structural network parameters; PDB mining of human cancer biomarkers and drug targets in parasites and bacteriaPREDICTING SUBCHLOROPLAST LOCATIONS OF PROTEINS BASED ON THE GENERAL FORM OF CHOU'S PSEUDO AMINO ACID COMPOSITION: APPROACHED FROM OPTIMAL TRIPEPTIDE COMPOSITION
P2860
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P2860
Prediction of G-protein-coupled receptor classes in low homology using Chou's pseudo amino acid composition with approximate entropy and hydrophobicity patterns.
description
2010 nî lūn-bûn
@nan
2010 թուականի Մայիսին հրատարակուած գիտական յօդուած
@hyw
2010 թվականի մայիսին հրատարակված գիտական հոդված
@hy
2010年の論文
@ja
2010年論文
@yue
2010年論文
@zh-hant
2010年論文
@zh-hk
2010年論文
@zh-mo
2010年論文
@zh-tw
2010年论文
@wuu
name
Prediction of G-protein-couple ...... y and hydrophobicity patterns.
@ast
Prediction of G-protein-couple ...... y and hydrophobicity patterns.
@en
Prediction of G-protein-couple ...... y and hydrophobicity patterns.
@nl
type
label
Prediction of G-protein-couple ...... y and hydrophobicity patterns.
@ast
Prediction of G-protein-couple ...... y and hydrophobicity patterns.
@en
Prediction of G-protein-couple ...... y and hydrophobicity patterns.
@nl
prefLabel
Prediction of G-protein-couple ...... y and hydrophobicity patterns.
@ast
Prediction of G-protein-couple ...... y and hydrophobicity patterns.
@en
Prediction of G-protein-couple ...... y and hydrophobicity patterns.
@nl
P2093
P1476
Prediction of G-protein-couple ...... y and hydrophobicity patterns.
@en
P2093
P304
P356
10.2174/092986610791112693
P577
2010-05-01T00:00:00Z