iDNA-Prot: identification of DNA binding proteins using random forest with grey model.
about
A survey of computational intelligence techniques in protein function predictionPrediction of protein domain with mRMR feature selection and analysisPredicting Anatomical Therapeutic Chemical (ATC) classification of drugs by integrating chemical-chemical interactions and similaritiesiSNO-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 networkingIdentification 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 channelsIdentification of DNA-binding proteins using support vector machine with sequence informationA multi-label predictor for identifying the subcellular locations of singleplex and multiplex eukaryotic proteinsnDNA-Prot: identification of DNA-binding proteins based on unbalanced classification.Reverse Engineering of Genome-wide Gene Regulatory Networks from Gene Expression DataiSS-PseDNC: identifying splicing sites using pseudo dinucleotide composition.enDNA-Prot: identification of DNA-binding proteins by applying ensemble learning.A computational algorithm for functional clustering of proteome dynamics during developmentAnalysis and prediction of single-stranded and double-stranded DNA binding proteins based on protein sequences.Photoaffinity labeling of transcription factors by DNA-templated crosslinking.iDNA-Prot|dis: identifying DNA-binding proteins by incorporating amino acid distance-pairs and reduced alphabet profile into the general pseudo amino acid compositionHepatitis 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.Cooperativity among short amyloid stretches in long amyloidogenic sequencesDigital IIR filters design using differential evolution algorithm with a controllable probabilistic population size.Applications of alignment-free methods in epigenomicsAn improved sequence based prediction protocol for DNA-binding proteins using SVM and comprehensive feature analysis.The application of the grey disaster model to forecast epidemic peaks of typhoid and paratyphoid fever in ChinaiPTM-mLys: identifying multiple lysine PTM sites and their different types.Predicting DNA-binding proteins and binding residues by complex structure prediction and application to human proteome.PNImodeler: web server for inferring protein-binding nucleotides from sequence data.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.Identification of DNA-binding proteins using multi-features fusion and binary firefly optimization algorithm.DNA binding protein identification by combining pseudo amino acid composition and profile-based protein representationDNABP: Identification of DNA-Binding Proteins Based on Feature Selection Using a Random Forest and Predicting Binding Residues.A sequence-based method to predict the impact of regulatory variants using random forest.HSP70 binding protein 1 (HspBP1) suppresses HIV-1 replication by inhibiting NF-κB mediated activation of viral gene expression.iRSpot-PseDNC: identify recombination spots with pseudo dinucleotide compositioniROS-gPseKNC: Predicting replication origin sites in DNA by incorporating dinucleotide position-specific propensity into general pseudo nucleotide composition.iCar-PseCp: identify carbonylation sites in proteins by Monte Carlo sampling and incorporating sequence coupled effects into general PseAAC.De-novo protein function prediction using DNA binding and RNA binding proteins as a test caseiHyd-PseCp: Identify hydroxyproline and hydroxylysine in proteins by incorporating sequence-coupled effects into general PseAAC.iPhos-PseEn: identifying phosphorylation sites in proteins by fusing different pseudo components into an ensemble classifier.
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iDNA-Prot: identification of DNA binding proteins using random forest with grey model.
description
2011 nî lūn-bûn
@nan
2011 թուականի Սեպտեմբերին հրատարակուած գիտական յօդուած
@hyw
2011 թվականի սեպտեմբերին 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
iDNA-Prot: identification of DNA binding proteins using random forest with grey model.
@ast
iDNA-Prot: identification of DNA binding proteins using random forest with grey model.
@en
iDNA-Prot: identification of DNA binding proteins using random forest with grey model.
@nl
type
label
iDNA-Prot: identification of DNA binding proteins using random forest with grey model.
@ast
iDNA-Prot: identification of DNA binding proteins using random forest with grey model.
@en
iDNA-Prot: identification of DNA binding proteins using random forest with grey model.
@nl
prefLabel
iDNA-Prot: identification of DNA binding proteins using random forest with grey model.
@ast
iDNA-Prot: identification of DNA binding proteins using random forest with grey model.
@en
iDNA-Prot: identification of DNA binding proteins using random forest with grey model.
@nl
P2860
P1433
P1476
iDNA-Prot: identification of DNA binding proteins using random forest with grey model.
@en
P2093
Jian-An Fang
Wei-Zhong Lin
P2860
P304
P356
10.1371/JOURNAL.PONE.0024756
P407
P577
2011-09-15T00:00:00Z