2011
DOI: 10.1371/journal.pone.0018476
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Abstract: Antimicrobial peptides (AMPs) represent a class of natural peptides that form a part of the innate immune system, and this kind of ‘nature's antibiotics’ is quite promising for solving the problem of increasing antibiotic resistance. In view of this, it is highly desired to develop an effective computational method for accurately predicting novel AMPs because it can provide us with more candidates and useful insights for drug design. In this study, a new method for predicting AMPs was implemented by integratin… Show more

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Cited by 175 publications
(161 citation statements)
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References 88 publications
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“…Some authors have used predictive data mining to assess the antimicrobial potential of new peptides. For example the AMPer method (Fjell et al 2007) recognizes individual classes of AMPs (such as defensins, cathelicidins and cecropins) and discovers novel AMP candidates based on HMMs fed on publicly available data; the BACTIBASE method (Hammami et al 2007 uses HMMs to produce bacteriocin profiles for each known family and the sequence analysis tool HMMER to provide statistical descriptions of family consensus sequences in order to support sequence-based searches on the bacterial families producing bacteriocins; the AntiBP and AntiBP2 methods (Lata et al 2007(Lata et al , 2010) predict antibacterial peptides applying ANN, QM and SVM models to the analysis of the N and C terminal residues of proteins; the CAMP method (Thomas et al 2010) uses RF, DA and SVM models to predict the antimicrobial activity of peptide sequences; the BA-GEL2 method (de Jong et al 2010) combines HMMs and simple decision rules in the prediction of bacteriocin sub-classes; the DAMPD method (Sundararajan et al 2011) predicts AMPs based on SVMs that can classify peptides into one of 27 AMP families in catalogue; and the tool of Wang et al (2011b) integrates protein BLAST (BLASTP) and a feature selection method based on mRMR and IFS models to select the optimal features for the prediction of AMPs vs non-AMPs. In addition, Juretic´et al (2009,2011) have been addressing the correlation between the physical characteristics of natural AMPs and high selectivity to generate potential peptide antibiotics not homologous to any existing natural or synthetic AMPs.…”
Section: Discovery and Classification Of Ampsmentioning
confidence: 99%
“…Some authors have used predictive data mining to assess the antimicrobial potential of new peptides. For example the AMPer method (Fjell et al 2007) recognizes individual classes of AMPs (such as defensins, cathelicidins and cecropins) and discovers novel AMP candidates based on HMMs fed on publicly available data; the BACTIBASE method (Hammami et al 2007 uses HMMs to produce bacteriocin profiles for each known family and the sequence analysis tool HMMER to provide statistical descriptions of family consensus sequences in order to support sequence-based searches on the bacterial families producing bacteriocins; the AntiBP and AntiBP2 methods (Lata et al 2007(Lata et al , 2010) predict antibacterial peptides applying ANN, QM and SVM models to the analysis of the N and C terminal residues of proteins; the CAMP method (Thomas et al 2010) uses RF, DA and SVM models to predict the antimicrobial activity of peptide sequences; the BA-GEL2 method (de Jong et al 2010) combines HMMs and simple decision rules in the prediction of bacteriocin sub-classes; the DAMPD method (Sundararajan et al 2011) predicts AMPs based on SVMs that can classify peptides into one of 27 AMP families in catalogue; and the tool of Wang et al (2011b) integrates protein BLAST (BLASTP) and a feature selection method based on mRMR and IFS models to select the optimal features for the prediction of AMPs vs non-AMPs. In addition, Juretic´et al (2009,2011) have been addressing the correlation between the physical characteristics of natural AMPs and high selectivity to generate potential peptide antibiotics not homologous to any existing natural or synthetic AMPs.…”
Section: Discovery and Classification Of Ampsmentioning
confidence: 99%
“…Banking and Finance Sector [18], [19], [20], [21] Insurance risk Classification Insurance [22] Parkinson's disease , Medical Diagnosis of Cardio vascular disease, Cancer Diagnosis Prediction of antimicrobial peptides (Natural anti biotic) Breast cancer diagnosis Early detection of the Alzheimer's disease Brain tumor detection Medical Science, Bioinformatics [23], [24], [25], [26], [27], [32] Sentiment Analysis in multiple language Text Clustering Text Classification Emotion recognition from speech Spam Filtering…”
Section: Application / Business Domain Referencementioning
confidence: 99%
“…With the further rapid development of new sequencing technology, the biological applications become more and more widely, including exposition of relationship between nucleosome positioning and DNA methylation [1], prediction of missense mutation or protein functionality [2,3], the assembly of new genomes [4], crop breeding [5], and so on. For most of these applications, multiple sequence alignments are fundamental.…”
Section: Introductionmentioning
confidence: 99%