Gpos-mPLoc: a top-down approach to improve the quality of predicting subcellular localization of Gram-positive bacterial proteins.
about
A multi-label predictor for identifying the subcellular locations of singleplex and multiplex eukaryotic proteinsGene ontology based transfer learning for protein subcellular localizationPre-absorbed immunoproteomics: a novel method for the detection of Streptococcus suis surface proteins.Subcellular localization of extracytoplasmic proteins in monoderm bacteria: rational secretomics-based strategy for genomic and proteomic analyses.Identification of peptidoglycan-associated proteins as vaccine candidates for enterococcal infections.Multi-location gram-positive and gram-negative bacterial protein subcellular localization using gene ontology and multi-label classifier ensemble.Profiling and Identification of Novel Immunogenic Proteins of Staphylococcus hyicus ZC-4 by Immunoproteomic Assay.Identifying and characterising PPE7 (Rv0354c) high activity binding peptides and their role in inhibiting cell invasion.Predicting Subcellular Localization of Apoptosis Proteins Combining GO Features of Homologous Proteins and Distance Weighted KNN Classifier.Predicting multisite protein subcellular locations: progress and challenges.Multi-iPPseEvo: A Multi-label Classifier for Identifying Human Phosphorylated Proteins by Incorporating Evolutionary Information into Chou's General PseAAC via Grey System Theory.Imbalanced multi-label learning for identifying antimicrobial peptides and their functional types.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.Protein Sorting Prediction.Predicting Subcellular Localization of Proteins by Bioinformatic Algorithms.pLoc-mAnimal: predict subcellular localization of animal proteins with both single and multiple sites.Classifying Multifunctional Enzymes by Incorporating Three Different Models into Chou's General Pseudo Amino Acid Composition.Unraveling Gardnerella vaginalis Surface Proteins Using Cell Shaving Proteomics.PREDICTING SUBCHLOROPLAST LOCATIONS OF PROTEINS BASED ON THE GENERAL FORM OF CHOU'S PSEUDO AMINO ACID COMPOSITION: APPROACHED FROM OPTIMAL TRIPEPTIDE COMPOSITION
P2860
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P2860
Gpos-mPLoc: a top-down approach to improve the quality of predicting subcellular localization of Gram-positive bacterial proteins.
description
2009 nî lūn-bûn
@nan
2009年の論文
@ja
2009年論文
@yue
2009年論文
@zh-hant
2009年論文
@zh-hk
2009年論文
@zh-mo
2009年論文
@zh-tw
2009年论文
@wuu
2009年论文
@zh
2009年论文
@zh-cn
name
Gpos-mPLoc: a top-down approac ...... m-positive bacterial proteins.
@en
type
label
Gpos-mPLoc: a top-down approac ...... m-positive bacterial proteins.
@en
prefLabel
Gpos-mPLoc: a top-down approac ...... m-positive bacterial proteins.
@en
P1476
Gpos-mPLoc: a top-down approac ...... m-positive bacterial proteins.
@en
P304
P356
10.2174/092986609789839322
P577
2009-01-01T00:00:00Z