Virus-mPLoc: a fusion classifier for viral protein subcellular location prediction by incorporating multiple sites.
about
A multi-label predictor for identifying the subcellular locations of singleplex and multiplex eukaryotic proteinsmGOASVM: Multi-label protein subcellular localization based on gene ontology and support vector machinesImbalanced multi-modal multi-label learning for subcellular localization prediction of human proteins with both single and multiple sites.Multi-label multi-kernel transfer learning for human protein subcellular localization.Mining Proteins with Non-Experimental Annotations Based on an Active Sample Selection Strategy for Predicting Protein Subcellular Localization.HybridGO-Loc: mining hybrid features on gene ontology for predicting subcellular localization of multi-location proteinsCharacterization of the 55-residue protein encoded by the 9S E1A mRNA of species C adenovirus.Predicting Subcellular Localization of Apoptosis Proteins Combining GO Features of Homologous Proteins and Distance Weighted KNN Classifier.iROS-gPseKNC: Predicting replication origin sites in DNA by incorporating dinucleotide position-specific propensity into general pseudo nucleotide composition.PseAAC-General: fast building various modes of general form of Chou's pseudo-amino acid composition for large-scale protein datasets.Recent progress in predicting protein sub-subcellular locations.Predicting multisite protein subcellular locations: progress and challenges.iPhos-PseEvo: Identifying Human Phosphorylated Proteins by Incorporating Evolutionary Information into General PseAAC via Grey System Theory.Protein (multi-)location prediction: using location inter-dependencies in a probabilistic frameworkIdentification of potential vaccine candidates against Streptococcus pneumoniae by reverse vaccinology approach.Multi-iPPseEvo: A Multi-label Classifier for Identifying Human Phosphorylated Proteins by Incorporating Evolutionary Information into Chou's General PseAAC via Grey System Theory.MSLVP: prediction of multiple subcellular localization of viral proteins using a support vector machine.The effect of three novel feature extraction methods on the prediction of the subcellular localization of multi-site virus proteins.Imbalanced multi-label learning for identifying antimicrobial peptides and their functional types.iMiRNA-PseDPC: microRNA precursor identification with a pseudo distance-pair composition approach.A method to distinguish between lysine acetylation and lysine ubiquitination with feature selection and analysis.iATC-mHyb: a hybrid multi-label classifier for predicting the classification of anatomical therapeutic chemicals.A multilabel model based on Chou's pseudo-amino acid composition for identifying membrane proteins with both single and multiple functional types.Identifying the singleplex and multiplex proteins based on transductive learning for protein subcellular localization prediction.Identification of DNA-binding proteins by incorporating evolutionary information into pseudo amino acid composition via the top-n-gram approach.pLoc-mHum: predict subcellular localization of multi-location human proteins via general PseAAC to winnow out the crucial GO information.pLoc-mAnimal: predict subcellular localization of animal proteins with both single and multiple sites.
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Virus-mPLoc: a fusion classifier for viral protein subcellular location prediction by incorporating multiple sites.
description
2010 nî lūn-bûn
@nan
2010年の論文
@ja
2010年論文
@yue
2010年論文
@zh-hant
2010年論文
@zh-hk
2010年論文
@zh-mo
2010年論文
@zh-tw
2010年论文
@wuu
2010年论文
@zh
2010年论文
@zh-cn
name
Virus-mPLoc: a fusion classifi ...... incorporating multiple sites.
@en
type
label
Virus-mPLoc: a fusion classifi ...... incorporating multiple sites.
@en
prefLabel
Virus-mPLoc: a fusion classifi ...... incorporating multiple sites.
@en
P2860
P1476
Virus-mPLoc: a fusion classifi ...... incorporating multiple sites.
@en
P2860
P304
P356
10.1080/07391102.2010.10507351
P577
2010-10-01T00:00:00Z