Prediction of cell penetrating peptides by support vector machines
about
Systematic analysis and prediction of pupylation sites in prokaryotic proteinsChemical-functional diversity in cell-penetrating peptidesCPPsite 2.0: a repository of experimentally validated cell-penetrating peptides.Cheminformatics-aided discovery of small-molecule Protein-Protein Interaction (PPI) dual inhibitors of Tumor Necrosis Factor (TNF) and Receptor Activator of NF-κB Ligand (RANKL).Exploring general-purpose protein features for distinguishing enzymes and non-enzymes within the twilight zone.FindFoci: a focus detection algorithm with automated parameter training that closely matches human assignments, reduces human inconsistencies and increases speed of analysisBiochemical and Cellular Specificity of Peptide Inhibitors of G Protein-Coupled Receptor Kinases.Antimicrobial peptides design by evolutionary multiobjective optimizationNovel central nervous system drug delivery systems.The J-domain of heat shock protein 40 can enhance the transduction efficiency of arginine-rich cell-penetrating peptides.Identification of a Short Cell-Penetrating Peptide from Bovine Lactoferricin for Intracellular Delivery of DNA in Human A549 Cells.Prediction of aptamer-protein interacting pairs using an ensemble classifier in combination with various protein sequence attributesEfficient therapeutic delivery by a novel cell-permeant peptide derived from KDM4A protein for antitumor and antifibrosis.Cell-penetrating peptides: A tool for effective delivery in gene-targeted therapies.Cell penetration: scope and limitations by the application of cell-penetrating peptides.Hemolytik: a database of experimentally determined hemolytic and non-hemolytic peptides.In silico approaches for designing highly effective cell penetrating peptides.Computational approach for designing tumor homing peptides.AVP-IC50 Pred: Multiple machine learning techniques-based prediction of peptide antiviral activity in terms of half maximal inhibitory concentration (IC50).An in silico platform for predicting, screening and designing of antihypertensive peptides.MLACP: machine-learning-based prediction of anticancer peptides.CPPpred: prediction of cell penetrating peptides.Prediction and analysis of cell-penetrating peptides using pseudo-amino acid composition and random forest models.SkipCPP-Pred: an improved and promising sequence-based predictor for predicting cell-penetrating peptides.Identification of immunoglobulins using Chou's pseudo amino acid composition with feature selection technique.Prediction of Cell-Penetrating Potential of Modified Peptides Containing Natural and Chemically Modified Residues.Machine Learning To Predict Cell-Penetrating Peptides for Antisense Delivery.
P2860
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P2860
Prediction of cell penetrating peptides by support vector machines
description
2011 nî lūn-bûn
@nan
2011 թուականի Յուլիսին հրատարակուած գիտական յօդուած
@hyw
2011 թվականի հուլիսին 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
Prediction of cell penetrating peptides by support vector machines
@ast
Prediction of cell penetrating peptides by support vector machines
@en
Prediction of cell penetrating peptides by support vector machines
@nl
type
label
Prediction of cell penetrating peptides by support vector machines
@ast
Prediction of cell penetrating peptides by support vector machines
@en
Prediction of cell penetrating peptides by support vector machines
@nl
prefLabel
Prediction of cell penetrating peptides by support vector machines
@ast
Prediction of cell penetrating peptides by support vector machines
@en
Prediction of cell penetrating peptides by support vector machines
@nl
P2093
P2860
P3181
P1476
Prediction of cell penetrating peptides by support vector machines
@en
P2093
C Ian Johnston
Kenneth O Willeford
Shane C Burgess
Susan M Bridges
William S Sanders
P2860
P304
P3181
P356
10.1371/JOURNAL.PCBI.1002101
P407
P577
2011-07-01T00:00:00Z