An improved sequence based prediction protocol for DNA-binding proteins using SVM and comprehensive feature analysis.
about
An ensemble method with hybrid features to identify extracellular matrix proteinsJPPRED: Prediction of Types of J-Proteins from Imbalanced Data Using an Ensemble Learning Method.iDNA-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 transformationA feature selection method for classification within functional genomics experiments based on the proportional overlapping score.Predict and Analyze Protein Glycation Sites with the mRMR and IFS MethodsPR2ALIGN: a stand-alone software program and a web-server for protein sequence alignment using weighted biochemical properties of amino acidsIdentification of DNA-binding proteins using multi-features fusion and binary firefly optimization algorithm.Sequence Based Prediction of Antioxidant Proteins Using a Classifier Selection Strategy.An Ensemble Method to Distinguish Bacteriophage Virion from Non-Virion Proteins Based on Protein Sequence CharacteristicsSequence-Based Prediction of RNA-Binding Proteins Using Random Forest with Minimum Redundancy Maximum Relevance Feature SelectionDNABP: Identification of DNA-Binding Proteins Based on Feature Selection Using a Random Forest and Predicting Binding Residues.BacHbpred: Support Vector Machine Methods for the Prediction of Bacterial Hemoglobin-Like Proteins.Analysis and prediction of drug-drug interaction by minimum redundancy maximum relevance and incremental feature selection.DNA-binding protein prediction using plant specific support vector machines: validation and application of a new genome annotation tool.Sequence based prediction of DNA-binding proteins based on hybrid feature selection using random forest and Gaussian naïve Bayes.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.PseDNA-Pro: DNA-Binding Protein Identification by Combining Chou's PseAAC and Physicochemical Distance Transformation.On the prediction of DNA-binding proteins only from primary sequences: A deep learning approach.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
An improved sequence based prediction protocol for DNA-binding proteins using SVM and comprehensive feature analysis.
description
2013 nî lūn-bûn
@nan
2013 թուականի Մարտին հրատարակուած գիտական յօդուած
@hyw
2013 թվականի մարտին հրատարակված գիտական հոդված
@hy
2013年の論文
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2013年論文
@yue
2013年論文
@zh-hant
2013年論文
@zh-hk
2013年論文
@zh-mo
2013年論文
@zh-tw
2013年论文
@wuu
name
An improved sequence based pre ...... omprehensive feature analysis.
@ast
An improved sequence based pre ...... omprehensive feature analysis.
@en
An improved sequence based pre ...... omprehensive feature analysis.
@nl
type
label
An improved sequence based pre ...... omprehensive feature analysis.
@ast
An improved sequence based pre ...... omprehensive feature analysis.
@en
An improved sequence based pre ...... omprehensive feature analysis.
@nl
prefLabel
An improved sequence based pre ...... omprehensive feature analysis.
@ast
An improved sequence based pre ...... omprehensive feature analysis.
@en
An improved sequence based pre ...... omprehensive feature analysis.
@nl
P2093
P2860
P356
P1433
P1476
An improved sequence based pre ...... omprehensive feature analysis.
@en
P2093
Chuanxin Zou
Honglin Li
Jiayu Gong
P2860
P2888
P356
10.1186/1471-2105-14-90
P577
2013-03-09T00:00:00Z
P5875
P6179
1044624611