Prediction of antimicrobial peptides based on the adaptive neuro-fuzzy inference system application.
about
Class IIa bacteriocins: diversity and new developmentsStructural insights into Cn-AMP1, a short disulfide-free multifunctional peptide from green coconut waterAnalysis and prediction of the critical regions of antimicrobial peptides based on conditional random fieldsOptical and dielectric sensors based on antimicrobial peptides for microorganism diagnosis.CS-AMPPred: an updated SVM model for antimicrobial activity prediction in cysteine-stabilized peptides.Antitumor and antimicrobial activity of some cyclic tetrapeptides and tripeptides derived from marine bacteria.Empirical comparison of web-based antimicrobial peptide prediction tools.Screening and Optimizing Antimicrobial Peptides by Using SPOT-Synthesis.AVP-IC50 Pred: Multiple machine learning techniques-based prediction of peptide antiviral activity in terms of half maximal inhibitory concentration (IC50).Deep Learning Improves Antimicrobial Peptide Recognition.Optimal selection of molecular descriptors for antimicrobial peptides classification: an evolutionary feature weighting approach
P2860
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P2860
Prediction of antimicrobial peptides based on the adaptive neuro-fuzzy inference system application.
description
2012 nî lūn-bûn
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2012年の論文
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2012年学术文章
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2012年学术文章
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2012年学术文章
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2012年学术文章
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name
Prediction of antimicrobial pe ...... inference system application.
@en
Prediction of antimicrobial pe ...... inference system application.
@nl
type
label
Prediction of antimicrobial pe ...... inference system application.
@en
Prediction of antimicrobial pe ...... inference system application.
@nl
prefLabel
Prediction of antimicrobial pe ...... inference system application.
@en
Prediction of antimicrobial pe ...... inference system application.
@nl
P2093
P2860
P356
P1433
P1476
Prediction of antimicrobial pe ...... inference system application.
@en
P2093
Daniel J Rigden
Fabiano C Fernandes
Octavio L Franco
P2860
P304
P356
10.1002/BIP.22066
P577
2012-01-01T00:00:00Z