Prediction of GABAA receptor proteins using the concept of Chou's pseudo-amino acid composition and support vector machine.
about
Classification and analysis of regulatory pathways using graph property, biochemical and physicochemical property, and functional propertyPrediction 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 similaritiesPredicting 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 compositioniGPCR-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 compositionGene Ontology and KEGG Pathway Enrichment Analysis of a Drug Target-Based Classification SystemThe Use of Gene Ontology Term and KEGG Pathway Enrichment for Analysis of Drug Half-LifeiCTX-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 proteinsComputational prediction of anti HIV-1 peptides and in vitro evaluation of anti HIV-1 activity of HIV-1 P24-derived peptides.Classification of DNA minor and major grooves binding proteins according to the NLSs by data analysis methods.iMethyl-PseAAC: identification of protein methylation sites via a pseudo amino acid composition approachiSS-PseDNC: identifying splicing sites using pseudo dinucleotide composition.iDNA-Prot: identification of DNA binding proteins using random forest with grey model.iDNA-Prot|dis: identifying DNA-binding proteins by incorporating amino acid distance-pairs and reduced alphabet profile into the general pseudo amino acid compositionAnalysis of tumor suppressor genes based on gene ontology and the KEGG pathway.Identification of amino acid propensities that are strong determinants of linear B-cell epitope using neural networks.Hepatitis C virus network based classification of hepatocellular cirrhosis and carcinomaIdentification of colorectal cancer related genes with mRMR and shortest path in protein-protein interaction network.Prediction of metalloproteinase family based on the concept of Chou's pseudo amino acid composition using a machine learning approach.Imbalanced multi-modal multi-label learning for subcellular localization prediction of human proteins with both single and multiple sites.iPro54-PseKNC: a sequence-based predictor for identifying sigma-54 promoters in prokaryote with pseudo k-tuple nucleotide composition.Gram-positive and Gram-negative subcellular localization using rotation forest and physicochemical-based featuresPrediction of drug indications based on chemical interactions and chemical similarities.Prediction of protein-protein interactions with clustered amino acids and weighted sparse representation.Multi-location gram-positive and gram-negative bacterial protein subcellular localization using gene ontology and multi-label classifier ensemble.Analysis of Gene Expression Profiles in the Human Brain Stem, Cerebellum and Cerebral CortexIdentification of compound-protein interactions through the analysis of gene ontology, KEGG enrichment for proteins and molecular fragments of compounds.In Silico Prediction of Gamma-Aminobutyric Acid Type-A Receptors Using Novel Machine-Learning-Based SVM and GBDT ApproachesiRSpot-PseDNC: identify recombination spots with pseudo dinucleotide compositionNaïve Bayes classifier with feature selection to identify phage virion proteinsiACP: a sequence-based tool for identifying anticancer peptides.Predicting drugs side effects based on chemical-chemical interactions and protein-chemical interactions.Prediction of effective drug combinations by chemical interaction, protein interaction and target enrichment of KEGG pathways.Analysis of Important Gene Ontology Terms and Biological Pathways Related to Pancreatic Cancer.
P2860
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P2860
Prediction of GABAA receptor proteins using the concept of Chou's pseudo-amino acid composition and support vector machine.
description
2011 nî lūn-bûn
@nan
2011 թուականի Ապրիլին հրատարակուած գիտական յօդուած
@hyw
2011 թվականի ապրիլին հրատարակված գիտական հոդված
@hy
2011年の論文
@ja
2011年論文
@yue
2011年論文
@zh-hant
2011年論文
@zh-hk
2011年論文
@zh-mo
2011年論文
@zh-tw
2011年论文
@wuu
name
Prediction of GABAA receptor p ...... on and support vector machine.
@ast
Prediction of GABAA receptor p ...... on and support vector machine.
@en
Prediction of GABAA receptor p ...... on and support vector machine.
@nl
type
label
Prediction of GABAA receptor p ...... on and support vector machine.
@ast
Prediction of GABAA receptor p ...... on and support vector machine.
@en
Prediction of GABAA receptor p ...... on and support vector machine.
@nl
prefLabel
Prediction of GABAA receptor p ...... on and support vector machine.
@ast
Prediction of GABAA receptor p ...... on and support vector machine.
@en
Prediction of GABAA receptor p ...... on and support vector machine.
@nl
P2093
P1476
Prediction of GABAA receptor p ...... on and support vector machine.
@en
P2093
Abolghasem Esmaeili
Hassan Mohabatkar
Majid Mohammad Beigi
P356
10.1016/J.JTBI.2011.04.017
P407
P577
2011-04-28T00:00:00Z