Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes.
about
A new method for predicting the subcellular localization of eukaryotic proteins with both single and multiple sites: Euk-mPLoc 2.0Predicting 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 networksPredicting functions of proteins in mouse based on weighted protein-protein interaction network and protein hybrid propertiesPrediction of antimicrobial peptides based on sequence alignment and feature selection methodsA comparison of computational methods for identifying virulence factorsiNuc-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 compositioniNitro-Tyr: prediction of nitrotyrosine sites in proteins with general pseudo amino acid compositionIdentification 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 networkingA multi-label predictor for identifying the subcellular locations of singleplex and multiplex eukaryotic proteinsProposing a highly accurate protein structural class predictor using segmentation-based features.Computational prediction of anti HIV-1 peptides and in vitro evaluation of anti HIV-1 activity of HIV-1 P24-derived peptides.Customised fragments libraries for protein structure prediction based on structural class annotations.Resistance gene identification from Larimichthys crocea with machine learning techniques.Identification of Multi-Functional Enzyme with Multi-Label ClassifierLECTINPred: web Server that Uses Complex Networks of Protein Structure for Prediction of Lectins with Potential Use as Cancer Biomarkers or in Parasite Vaccine Design.iFC²: an integrated web-server for improved prediction of protein structural class, fold type, and secondary structure content.Reverse Engineering of Genome-wide Gene Regulatory Networks from Gene Expression DataBenchmark data for identifying DNA methylation sites via pseudo trinucleotide composition.Efficacy of different protein descriptors in predicting protein functional families.Support Vector Machine-based method for predicting subcellular localization of mycobacterial proteins using evolutionary information and motifs.A machine learning approach for the identification of odorant binding proteins from sequence-derived propertiesMolecular cloning, characterization and regulation of two different NADH-glutamate synthase cDNAs in bean nodules.2L-piRNA: A Two-Layer Ensemble Classifier for Identifying Piwi-Interacting RNAs and Their Function.iMethyl-PseAAC: identification of protein methylation sites via a pseudo amino acid composition approachiHyd-PseAAC: predicting hydroxyproline and hydroxylysine in proteins by incorporating dipeptide position-specific propensity into pseudo amino acid compositionMISS-Prot: web server for self/non-self discrimination of protein residue networks in parasites; theory and experiments in Fasciola peptides and Anisakis allergens.Computational and experimental approaches to reveal the effects of single nucleotide polymorphisms with respect to disease diagnosticsPrediction of presynaptic and postsynaptic neurotoxins by combining various Chou's pseudo components.Exploring general-purpose protein features for distinguishing enzymes and non-enzymes within the twilight zone.Support vector machine prediction of enzyme function with conjoint triad feature and hierarchical context.iDNA-Prot|dis: identifying DNA-binding proteins by incorporating amino acid distance-pairs and reduced alphabet profile into the general pseudo amino acid compositionPredicting protein folding rates using the concept of Chou's pseudo amino acid composition.Prediction of metalloproteinase family based on the concept of Chou's pseudo amino acid composition using a machine learning approach.EHPred: an SVM-based method for epoxide hydrolases recognition and classification.An N-myristoylated globin with a redox-sensing function that regulates the defecation cycle in Caenorhabditis elegans.
P2860
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P2860
Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes.
description
2004 nî lūn-bûn
@nan
2004 թուականի Օգոստոսին հրատարակուած գիտական յօդուած
@hyw
2004 թվականի օգոստոսին հրատարակված գիտական հոդված
@hy
2004年の論文
@ja
2004年学术文章
@wuu
2004年学术文章
@zh-cn
2004年学术文章
@zh-hans
2004年学术文章
@zh-my
2004年学术文章
@zh-sg
2004年學術文章
@yue
name
Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes.
@ast
Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes.
@en
Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes.
@nl
type
label
Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes.
@ast
Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes.
@en
Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes.
@nl
prefLabel
Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes.
@ast
Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes.
@en
Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes.
@nl
P356
P1433
P1476
Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes.
@en
P356
10.1093/BIOINFORMATICS/BTH466
P407
P577
2004-08-12T00:00:00Z