GPCR-2L: predicting G protein-coupled receptors and their types by hybridizing two different modes of pseudo amino acid compositions.
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Bioinformatics approaches for functional annotation of membrane proteinsPrediction of protein domain with mRMR feature selection and analysisDesign novel dual agonists for treating type-2 diabetes by targeting peroxisome proliferator-activated receptors with core hopping approachiGPCR-drug: a web server for predicting interaction between GPCRs and drugs in cellular networkingiCTX-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 proteinsReverse Engineering of Genome-wide Gene Regulatory Networks from Gene Expression Data2L-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 approachIdentification 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 carcinomaImbalanced multi-modal multi-label learning for subcellular localization prediction of human proteins with both single and multiple sites.Digital IIR filters design using differential evolution algorithm with a controllable probabilistic population size.A high performance prediction of HPV genotypes by Chaos game representation and singular value decomposition.3D QSAR pharmacophore modeling, in silico screening, and density functional theory (DFT) approaches for identification of human chymase inhibitors.Multi-location gram-positive and gram-negative bacterial protein subcellular localization using gene ontology and multi-label classifier ensemble.iACP: a sequence-based tool for identifying anticancer peptides.iRSpot-TNCPseAAC: identify recombination spots with trinucleotide composition and pseudo amino acid componentsiNR-Drug: predicting the interaction of drugs with nuclear receptors in cellular networking.Recent advances in protein-protein interaction prediction: experimental and computational methods.Pseudo nucleotide composition or PseKNC: an effective formulation for analyzing genomic sequences.iPhos-PseEvo: Identifying Human Phosphorylated Proteins by Incorporating Evolutionary Information into General PseAAC via Grey System Theory.Classification of lung cancer using ensemble-based feature selection and machine learning methods.Find novel dual-agonist drugs for treating type 2 diabetes by means of cheminformatics.A novel feature ranking method for prediction of cancer stages using proteomics data.Systematic analysis of human lysine acetylation proteins and accurate prediction of human lysine acetylation through bi-relative adapted binomial score Bayes feature representation.A multilabel model based on Chou's pseudo-amino acid composition for identifying membrane proteins with both single and multiple functional types.Prediction of protein secondary structure using feature selection and analysis approach.iPromoter-2L: a two-layer predictor for identifying promoters and their types by multi-window-based PseKNC.Swfoldrate: predicting protein folding rates from amino acid sequence with sliding window method.Naïve Bayes QSDR classification based on spiral-graph Shannon entropies for protein biomarkers in human colon cancer.Wavelet images and Chou's pseudo amino acid composition for protein classification.iLoc-Plant: a multi-label classifier for predicting the subcellular localization of plant proteins with both single and multiple sites.
P2860
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P2860
GPCR-2L: predicting G protein-coupled receptors and their types by hybridizing two different modes of pseudo amino acid compositions.
description
2010 nî lūn-bûn
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2010年の論文
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2010年学术文章
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2010年学术文章
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2010年学术文章
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name
GPCR-2L: predicting G protein- ...... seudo amino acid compositions.
@en
GPCR-2L: predicting G protein- ...... seudo amino acid compositions.
@nl
type
label
GPCR-2L: predicting G protein- ...... seudo amino acid compositions.
@en
GPCR-2L: predicting G protein- ...... seudo amino acid compositions.
@nl
prefLabel
GPCR-2L: predicting G protein- ...... seudo amino acid compositions.
@en
GPCR-2L: predicting G protein- ...... seudo amino acid compositions.
@nl
P2860
P356
P1433
P1476
GPCR-2L: predicting G protein- ...... seudo amino acid compositions.
@en
P2093
P2860
P304
P356
10.1039/C0MB00170H
P577
2010-12-23T00:00:00Z