iRSpot-TNCPseAAC: identify recombination spots with trinucleotide composition and pseudo amino acid components
about
iNitro-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 channelsIdentification of Damaging nsSNVs in HumanERCC2 Gene.PseKNC-General: a cross-platform package for generating various modes of pseudo nucleotide compositions.Reverse Engineering of Genome-wide Gene Regulatory Networks from Gene Expression Data2L-piRNA: A Two-Layer Ensemble Classifier for Identifying Piwi-Interacting RNAs and Their Function.Quad-PRE: a hybrid method to predict protein quaternary structure attributes.iSS-PseDNC: identifying splicing sites using pseudo dinucleotide composition.iHyd-PseAAC: predicting hydroxyproline and hydroxylysine in proteins by incorporating dipeptide position-specific propensity into pseudo amino acid compositionPSNO: predicting cysteine S-nitrosylation sites by incorporating various sequence-derived features into the general form of Chou's PseAAC.iDNA-Prot|dis: identifying DNA-binding proteins by incorporating amino acid distance-pairs and reduced alphabet profile into the general pseudo amino acid compositioniPro54-PseKNC: a sequence-based predictor for identifying sigma-54 promoters in prokaryote with pseudo k-tuple nucleotide composition.Sequence-based identification of recombination spots using pseudo nucleic acid representation and recursive feature extraction by linear kernel SVMA high performance prediction of HPV genotypes by Chaos game representation and singular value decomposition.repDNA: a Python package to generate various modes of feature vectors for DNA sequences by incorporating user-defined physicochemical properties and sequence-order effects.Prediction of aptamer-protein interacting pairs using an ensemble classifier in combination with various protein sequence attributesEnvironmental genes and genomes: understanding the differences and challenges in the approaches and software for their analyses.A genetic algorithm-based weighted ensemble method for predicting transposon-derived piRNAsAn Effective Antifreeze Protein Predictor with Ensemble Classifiers and Comprehensive Sequence Descriptors.Pse-Analysis: a python package for DNA/RNA and protein/ peptide sequence analysis based on pseudo components and kernel methods.Predicting DNA Methylation State of CpG Dinucleotide Using Genome Topological Features and Deep Networks.Prediction of Protein Structural Class Based on Gapped-Dipeptides and a Recursive Feature Selection Approach.Predicting Subcellular Localization of Apoptosis Proteins Combining GO Features of Homologous Proteins and Distance Weighted KNN Classifier.iACP: a sequence-based tool for identifying anticancer peptides.ProFold: Protein Fold Classification with Additional Structural Features and a Novel Ensemble ClassifierRelapse-related long non-coding RNA signature to improve prognosis prediction of lung adenocarcinomaiROS-gPseKNC: Predicting replication origin sites in DNA by incorporating dinucleotide position-specific propensity into general pseudo nucleotide composition.Prediction of protein-protein interaction with pairwise kernel support vector machine.iNR-Drug: predicting the interaction of drugs with nuclear receptors in cellular networking.Protein submitochondrial localization from integrated sequence representation and SVM-based backward feature extraction.Pseudo nucleotide composition or PseKNC: an effective formulation for analyzing genomic sequences.Prediction of Protein Submitochondrial Locations by Incorporating Dipeptide Composition into Chou's General Pseudo Amino Acid Composition.iOri-Human: identify human origin of replication by incorporating dinucleotide physicochemical properties into pseudo nucleotide composition.Prediction of nucleosome positioning by the incorporation of frequencies and distributions of three different nucleotide segment lengths into a general pseudo k-tuple nucleotide composition.iRSpot-EL: identify recombination spots with an ensemble learning approach.Combining pseudo dinucleotide composition with the Z curve method to improve the accuracy of predicting DNA elements: a case study in recombination spots.Surveying and benchmarking techniques to analyse DNA gel fingerprint images.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.
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iRSpot-TNCPseAAC: identify recombination spots with trinucleotide composition and pseudo amino acid components
description
article científic
@ca
article scientifique
@fr
articolo scientifico
@it
artigo científico
@pt
bilimsel makale
@tr
scientific article published on 24 January 2014
@en
vedecký článok
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vetenskaplig artikel
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videnskabelig artikel
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vědecký článek
@cs
name
iRSpot-TNCPseAAC: identify rec ...... d pseudo amino acid components
@en
iRSpot-TNCPseAAC: identify rec ...... pseudo amino acid components.
@nl
type
label
iRSpot-TNCPseAAC: identify rec ...... d pseudo amino acid components
@en
iRSpot-TNCPseAAC: identify rec ...... pseudo amino acid components.
@nl
prefLabel
iRSpot-TNCPseAAC: identify rec ...... d pseudo amino acid components
@en
iRSpot-TNCPseAAC: identify rec ...... pseudo amino acid components.
@nl
P2860
P356
P1476
iRSpot-TNCPseAAC: identify rec ...... d pseudo amino acid components
@en
P2093
Wang-Ren Qiu
P2860
P304
P356
10.3390/IJMS15021746
P407
P577
2014-01-24T00:00:00Z