Prediction of protein cellular attributes using pseudo-amino acid composition.
about
Plant-mPLoc: a top-down strategy to augment the power for predicting plant protein subcellular localizationSemi-supervised protein subcellular localizationA new method for predicting the subcellular localization of eukaryotic proteins with both single and multiple sites: Euk-mPLoc 2.0LocTree2 predicts localization for all domains of lifePredicting subcellular localization of proteins for Gram-negative bacteria by support vector machines based on n-peptide compositionsEsub8: a novel tool to predict protein subcellular localizations in eukaryotic organismsProtein subcellular localization prediction for Gram-negative bacteria using amino acid subalphabets and a combination of multiple support vector machines.pSLIP: SVM based protein subcellular localization prediction using multiple physicochemical properties.Prediction of protein structural class with Rough Sets.An SVM-based system for predicting protein subnuclear localizations.iLoc-Euk: a multi-label classifier for predicting the subcellular localization of singleplex and multiplex eukaryotic proteinsPredicting drug-target interaction networks based on functional groups and biological featuresAnalysis and prediction of the metabolic stability of proteins based on their sequential features, subcellular locations and interaction networksPrediction of body fluids where proteins are secreted into based on protein interaction networkPredicting transcriptional activity of multiple site p53 mutants based on hybrid propertiesNR-2L: a two-level predictor for identifying nuclear receptor subfamilies based on sequence-derived featuresPredicting functions of proteins in mouse based on weighted protein-protein interaction network and protein hybrid propertiesClassification and analysis of regulatory pathways using graph property, biochemical and physicochemical property, and functional propertyIdentification of potent EGFR inhibitors from TCM Database@TaiwanPrediction of antimicrobial peptides based on sequence alignment and feature selection methodsA multi-label classifier for predicting the subcellular localization of gram-negative bacterial proteins with both single and multiple sitesPrediction 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 matrixA comparison of computational methods for identifying virulence factorsPredicting Anatomical Therapeutic Chemical (ATC) classification of drugs by integrating chemical-chemical interactions and similaritiesiNuc-PhysChem: a sequence-based predictor for identifying nucleosomes via physicochemical propertiesPredicting secretory proteins of malaria parasite by incorporating sequence evolution information into pseudo amino acid composition via grey system modeliSNO-PseAAC: predict cysteine S-nitrosylation sites in proteins by incorporating position specific amino acid propensity into pseudo amino acid compositionC-PAmP: large scale analysis and database construction containing high scoring computationally predicted antimicrobial peptides for all the available plant speciesiGPCR-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 compositionAn ensemble method with hybrid features to identify extracellular matrix proteinsLearning from Heterogeneous Data Sources: An Application in Spatial ProteomicsIdentification of real microRNA precursors with a pseudo structure status composition approachiCTX-type: a sequence-based predictor for identifying the types of conotoxins in targeting ion channelsiEzy-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 proteinsA multi-label predictor for identifying the subcellular locations of singleplex and multiplex eukaryotic proteinsIdentification of the core regulators of the HLA I-peptide binding processA predictor of membrane class: Discriminating alpha-helical and beta-barrel membrane proteins from non-membranous proteins
P2860
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P2860
Prediction of protein cellular attributes using pseudo-amino acid composition.
description
2001 nî lūn-bûn
@nan
2001 թուականի Մայիսին հրատարակուած գիտական յօդուած
@hyw
2001 թվականի մայիսին հրատարակված գիտական հոդված
@hy
2001年の論文
@ja
2001年論文
@yue
2001年論文
@zh-hant
2001年論文
@zh-hk
2001年論文
@zh-mo
2001年論文
@zh-tw
2001年论文
@wuu
name
Prediction of protein cellular attributes using pseudo-amino acid composition.
@ast
Prediction of protein cellular attributes using pseudo-amino acid composition.
@en
type
label
Prediction of protein cellular attributes using pseudo-amino acid composition.
@ast
Prediction of protein cellular attributes using pseudo-amino acid composition.
@en
prefLabel
Prediction of protein cellular attributes using pseudo-amino acid composition.
@ast
Prediction of protein cellular attributes using pseudo-amino acid composition.
@en
P356
P1433
P1476
Prediction of protein cellular attributes using pseudo-amino acid composition.
@en
P2093
P304
P356
10.1002/PROT.1035
P407
P577
2001-05-01T00:00:00Z