Prediction of metalloproteinase family based on the concept of Chou's pseudo amino acid composition using a machine learning approach.
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iNR-PhysChem: a sequence-based predictor for identifying nuclear receptors and their subfamilies via physical-chemical property matrixPredicting secretory proteins of malaria parasite by incorporating sequence evolution information into pseudo amino acid composition via grey system modeliSNO-PseAAC: predict cysteine S-nitrosylation sites in proteins by incorporating position specific amino acid propensity into pseudo amino acid compositioniGPCR-drug: a web server for predicting interaction between GPCRs and drugs in cellular networkingiNitro-Tyr: prediction of nitrotyrosine sites in proteins with general pseudo amino acid compositionIdentification of real microRNA precursors with a pseudo structure status composition approachiCTX-type: a sequence-based predictor for identifying the types of conotoxins in targeting ion channelsiEzy-drug: a web server for identifying the interaction between enzymes and drugs in cellular networkingiSS-PseDNC: identifying splicing sites using pseudo dinucleotide composition.iDNA-Prot|dis: identifying DNA-binding proteins by incorporating amino acid distance-pairs and reduced alphabet profile into the general pseudo amino acid compositioniRSpot-PseDNC: identify recombination spots with pseudo dinucleotide compositionNaïve Bayes classifier with feature selection to identify phage virion proteinsiACP: a sequence-based tool for identifying anticancer peptides.iRSpot-TNCPseAAC: identify recombination spots with trinucleotide composition and pseudo amino acid componentsPseAAC-General: fast building various modes of general form of Chou's pseudo-amino acid composition for large-scale protein datasets.iNR-Drug: predicting the interaction of drugs with nuclear receptors in cellular networking.Some remarks on predicting multi-label attributes in molecular biosystems.Pseudo nucleotide composition or PseKNC: an effective formulation for analyzing genomic sequences.iPhos-PseEvo: Identifying Human Phosphorylated Proteins by Incorporating Evolutionary Information into General PseAAC via Grey System Theory.Protein Remote Homology Detection by Combining Chou's Pseudo Amino Acid Composition and Profile-Based Protein Representation.Association of matrix metalloproteinase-10 polymorphisms with susceptibility to pelvic organ prolapse.Identification of protein-protein binding sites by incorporating the physicochemical properties and stationary wavelet transforms into pseudo amino acid composition.iRSpot-GAEnsC: identifing recombination spots via ensemble classifier and extending the concept of Chou's PseAAC to formulate DNA samples.DSPMP: Discriminating secretory proteins of malaria parasite by hybridizing different descriptors of Chou's pseudo amino acid patterns.Prediction of protein S-nitrosylation sites based on adapted normal distribution bi-profile Bayes and Chou's pseudo amino acid composition.A multilabel model based on Chou's pseudo-amino acid composition for identifying membrane proteins with both single and multiple functional types.repRNA: a web server for generating various feature vectors of RNA sequences.Learning protein multi-view features in complex space.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.Combining evolutionary information extracted from frequency profiles with sequence-based kernels for protein remote homology detection.Identification of immunoglobulins using Chou's pseudo amino acid composition with feature selection technique.iLoc-Animal: a multi-label learning classifier for predicting subcellular localization of animal proteins.Protein remote homology detection by combining Chou's distance-pair pseudo amino acid composition and principal component analysis.A Novel Hybrid Sequence-Based Model for Identifying Anticancer Peptides.iRSpot-Pse6NC: Identifying recombination spots in Saccharomyces cerevisiae by incorporating hexamer composition into general PseKNC.
P2860
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P2860
Prediction of metalloproteinase family based on the concept of Chou's pseudo amino acid composition using a machine learning approach.
description
2011 nî lūn-bûn
@nan
2011 թուականի Դեկտեմբերին հրատարակուած գիտական յօդուած
@hyw
2011 թվականի դեկտեմբերին 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 metalloproteinas ...... g a machine learning approach.
@ast
Prediction of metalloproteinas ...... g a machine learning approach.
@en
Prediction of metalloproteinas ...... g a machine learning approach.
@nl
type
label
Prediction of metalloproteinas ...... g a machine learning approach.
@ast
Prediction of metalloproteinas ...... g a machine learning approach.
@en
Prediction of metalloproteinas ...... g a machine learning approach.
@nl
prefLabel
Prediction of metalloproteinas ...... g a machine learning approach.
@ast
Prediction of metalloproteinas ...... g a machine learning approach.
@en
Prediction of metalloproteinas ...... g a machine learning approach.
@nl
P2093
P2860
P1476
Prediction of metalloproteinas ...... g a machine learning approach.
@en
P2093
Hassan Mohabatkar
Majid Mohammad Beigi
Mohaddeseh Behjati
P2860
P2888
P304
P356
10.1007/S10969-011-9120-4
P577
2011-12-03T00:00:00Z
P6179
1011529930