Sequence based prediction of DNA-binding proteins based on hybrid feature selection using random forest and Gaussian naïve Bayes.
about
A survey of computational intelligence techniques in protein function predictionHighly accurate sequence-based prediction of half-sphere exposures of amino acid residues in proteinsA property-based analysis of human transcription factorsiDNA-Prot|dis: identifying DNA-binding proteins by incorporating amino acid distance-pairs and reduced alphabet profile into the general pseudo amino acid compositionIdentifying DNA-binding proteins by combining support vector machine and PSSM distance transformationAn overview of the prediction of protein DNA-binding sitesPNImodeler: web server for inferring protein-binding nucleotides from sequence data.Identification of DNA-binding proteins using multi-features fusion and binary firefly optimization algorithm.DNA binding protein identification by combining pseudo amino acid composition and profile-based protein representationAn Effective Antifreeze Protein Predictor with Ensemble Classifiers and Comprehensive Sequence Descriptors.An Ensemble Method to Distinguish Bacteriophage Virion from Non-Virion Proteins Based on Protein Sequence CharacteristicsDNABP: Identification of DNA-Binding Proteins Based on Feature Selection Using a Random Forest and Predicting Binding Residues.iRSpot-GAEnsC: identifing recombination spots via ensemble classifier and extending the concept of Chou's PseAAC to formulate DNA samples.DNA-binding protein prediction using plant specific support vector machines: validation and application of a new genome annotation tool.PSFM-DBT: Identifying DNA-Binding Proteins by Combing Position Specific Frequency Matrix and Distance-Bigram Transformation.Improved detection of DNA-binding proteins via compression technology on PSSM information.Integrating sequence and gene expression information predicts genome-wide DNA-binding proteins and suggests a cooperative mechanism.HMMBinder: DNA-Binding Protein Prediction Using HMM Profile Based Features.On the prediction of DNA-binding proteins only from primary sequences: A deep learning approach.iDNAProt-ES: Identification of DNA-binding Proteins Using Evolutionary and Structural Features.Harnessing the evolutionary information on oxygen binding proteins through Support Vector Machines based modules.Using a Classifier Fusion Strategy to Identify Anti-angiogenic PeptidesA Model Stacking Framework for Identifying DNA Binding Proteins by Orchestrating Multi-View Features and Classifiers
P2860
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P2860
Sequence based prediction of DNA-binding proteins based on hybrid feature selection using random forest and Gaussian naïve Bayes.
description
2014 nî lūn-bûn
@nan
2014年の論文
@ja
2014年論文
@yue
2014年論文
@zh-hant
2014年論文
@zh-hk
2014年論文
@zh-mo
2014年論文
@zh-tw
2014年论文
@wuu
2014年论文
@zh
2014年论文
@zh-cn
name
Sequence based prediction of D ...... rest and Gaussian naïve Bayes.
@en
type
label
Sequence based prediction of D ...... rest and Gaussian naïve Bayes.
@en
prefLabel
Sequence based prediction of D ...... rest and Gaussian naïve Bayes.
@en
P2093
P2860
P1433
P1476
Sequence based prediction of D ...... rest and Gaussian naïve Bayes.
@en
P2093
Wangchao Lou
Xiaoqing Wang
Yixiao Chen
P2860
P304
P356
10.1371/JOURNAL.PONE.0086703
P407
P577
2014-01-24T00:00:00Z