iLoc-Plant: a multi-label classifier for predicting the subcellular localization of plant proteins with both single and multiple sites.
about
Prediction 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 modeliNitro-Tyr: prediction of nitrotyrosine sites in proteins with general pseudo amino acid compositionInvolvement of phosphatidate phosphatase in the biosynthesis of triacylglycerols in Chlamydomonas reinhardtiiLECTINPred: web Server that Uses Complex Networks of Protein Structure for Prediction of Lectins with Potential Use as Cancer Biomarkers or in Parasite Vaccine Design.Predicting antimicrobial peptides with improved accuracy by incorporating the compositional, physico-chemical and structural features into Chou's general PseAAC.Reverse Engineering of Genome-wide Gene Regulatory Networks from Gene Expression DatamGOASVM: Multi-label protein subcellular localization based on gene ontology and support vector machinesiSS-PseDNC: identifying splicing sites using pseudo dinucleotide composition.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.Imbalanced 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.iPTM-mLys: identifying multiple lysine PTM sites and their different types.PlantLoc: an accurate web server for predicting plant protein subcellular localization by substantiality motif.Identification and characterization of plastid-type proteins from sequence-attributed features using machine learning.HybridGO-Loc: mining hybrid features on gene ontology for predicting subcellular localization of multi-location proteinsSparse regressions for predicting and interpreting subcellular localization of multi-label proteinsPredicting Subcellular Localization of Apoptosis Proteins Combining GO Features of Homologous Proteins and Distance Weighted KNN Classifier.SubMito-PSPCP: predicting protein submitochondrial locations by hybridizing positional specific physicochemical properties with pseudoamino acid compositionsiROS-gPseKNC: Predicting replication origin sites in DNA by incorporating dinucleotide position-specific propensity into general pseudo nucleotide composition.iRSpot-TNCPseAAC: identify recombination spots with trinucleotide composition and pseudo amino acid componentsSome remarks on predicting multi-label attributes in molecular biosystems.Predicting multisite protein subcellular locations: progress and challenges.Characterization of proteins in S. cerevisiae with subcellular localizations.Predicting human protein subcellular locations by the ensemble of multiple predictors via protein-protein interaction network with edge clustering coefficients.pLoc-mPlant: predict subcellular localization of multi-location plant proteins by incorporating the optimal GO information into general PseAAC.Comprehensive comparative analysis and identification of RNA-binding protein domains: multi-class classification and feature selection.Pseudo nucleotide composition or PseKNC: an effective formulation for analyzing genomic sequences.Bioinformatics Analysis of Protein Secretion in Plants.MultiP-Apo: A Multilabel Predictor for Identifying Subcellular Locations of Apoptosis Proteins.iPhos-PseEvo: Identifying Human Phosphorylated Proteins by Incorporating Evolutionary Information into General PseAAC via Grey System Theory.Estimating the effects of transcription factors binding and histone modifications on gene expression levels in human cells.Protein (multi-)location prediction: using location inter-dependencies in a probabilistic frameworkClassification of lung cancer using ensemble-based feature selection and machine learning methods.iATC-mISF: a multi-label classifier for predicting the classes of anatomical therapeutic chemicals.Multi-iPPseEvo: A Multi-label Classifier for Identifying Human Phosphorylated Proteins by Incorporating Evolutionary Information into Chou's General PseAAC via Grey System Theory.Designer labels for plant metabolism: statistical design of isotope labeling experiments for improved quantification of flux in complex plant metabolic networks.
P2860
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P2860
iLoc-Plant: a multi-label classifier for predicting the subcellular localization of plant proteins with both single and multiple sites.
description
2011 nî lūn-bûn
@nan
2011年の論文
@ja
2011年学术文章
@wuu
2011年学术文章
@zh
2011年学术文章
@zh-cn
2011年学术文章
@zh-hans
2011年学术文章
@zh-my
2011年学术文章
@zh-sg
2011年學術文章
@yue
2011年學術文章
@zh-hant
name
iLoc-Plant: a multi-label clas ...... oth single and multiple sites.
@en
iLoc-Plant: a multi-label clas ...... oth single and multiple sites.
@nl
type
label
iLoc-Plant: a multi-label clas ...... oth single and multiple sites.
@en
iLoc-Plant: a multi-label clas ...... oth single and multiple sites.
@nl
prefLabel
iLoc-Plant: a multi-label clas ...... oth single and multiple sites.
@en
iLoc-Plant: a multi-label clas ...... oth single and multiple sites.
@nl
P2860
P356
P1433
P1476
iLoc-Plant: a multi-label clas ...... oth single and multiple sites.
@en
P2093
Zhi-Cheng Wu
P2860
P304
P356
10.1039/C1MB05232B
P577
2011-10-10T00:00:00Z