nDNA-Prot: identification of DNA-binding proteins based on unbalanced classification.
about
Survey of Programs Used to Detect Alternative Splicing Isoforms from Deep Sequencing Data In SilicoIdentification and Progression of Heart Disease Risk Factors in Diabetic Patients from Longitudinal Electronic Health RecordsPattern Recognition on Read Positioning in Next Generation Sequencing.Resistance gene identification from Larimichthys crocea with machine learning techniques.Identification of Multi-Functional Enzyme with Multi-Label ClassifierPretata: predicting TATA binding proteins with novel features and dimensionality reduction strategy.ZBIT Bioinformatics Toolbox: A Web-Platform for Systems Biology and Expression Data Analysis.Identifying DNA-binding proteins by combining support vector machine and PSSM distance transformationAccurate Prediction of Transposon-Derived piRNAs by Integrating Various Sequential and Physicochemical Features.SVM-Prot 2016: A Web-Server for Machine Learning Prediction of Protein Functional Families from Sequence Irrespective of Similarity.Identification of DNA-binding proteins using multi-features fusion and binary firefly optimization algorithm.In Silico Prediction of Gamma-Aminobutyric Acid Type-A Receptors Using Novel Machine-Learning-Based SVM and GBDT ApproachesSABinder: A Web Service for Predicting Streptavidin-Binding Peptides.DNA binding protein identification by combining pseudo amino acid composition and profile-based protein representationPPCM: Combing Multiple Classifiers to Improve Protein-Protein Interaction PredictionDNABP: Identification of DNA-Binding Proteins Based on Feature Selection Using a Random Forest and Predicting Binding Residues.An information-based network approach for protein classificationPredicting cancerlectins by the optimal g-gap dipeptides.Optimal classifier for imbalanced data using Matthews Correlation Coefficient metric.iMiRNA-SSF: Improving the Identification of MicroRNA Precursors by Combining Negative Sets with Different Distributions.A deformation energy-based model for predicting nucleosome dyads and occupancyProtein Remote Homology Detection Based on an Ensemble Learning Approach.On the Computational Power of Spiking Neural P Systems with Self-Organization.Identification of apolipoprotein using feature selection technique.iRSpot-DACC: a computational predictor for recombination hot/cold spots identification based on dinucleotide-based auto-cross covariancePeculiar Genes Selection: A new features selection method to improve classification performances in imbalanced data setsSimilarity computation strategies in the microRNA-disease network: a survey.Detecting representative data and generating synthetic samples to improve learning accuracy with imbalanced data sets.Adaptive Swarm Balancing Algorithms for rare-event prediction in imbalanced healthcare data.Predicting diabetes mellitus using SMOTE and ensemble machine learning approach: The Henry Ford ExercIse Testing (FIT) project.Construction and Identification of the RNAi Recombinant Lentiviral Vector Targeting Human DEPDC7 Gene.Prediction and validation of cis-regulatory elements in 5' upstream regulatory regions of lectin receptor-like kinase gene family in rice.Improving tRNAscan-SE Annotation Results via Ensemble Classifiers.Machine Learning of Protein Interactions in Fungal Secretory Pathways.Human Protein Subcellular Localization with Integrated Source and Multi-label Ensemble ClassifierIdentifying novel protein phenotype annotations by hybridizing protein-protein interactions and protein sequence similarities.The Virtual Screening of the Drug Protein with a Few Crystal Structures Based on the Adaboost-SVM.Predicting the subcellular localization of mycobacterial proteins by incorporating the optimal tripeptides into the general form of pseudo amino acid composition.PSFM-DBT: Identifying DNA-Binding Proteins by Combing Position Specific Frequency Matrix and Distance-Bigram Transformation.Predicting Variation of DNA Shape Preferences in Protein-DNA Interaction in Cancer Cells with a New Biophysical Model.
P2860
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P2860
nDNA-Prot: identification of DNA-binding proteins based on unbalanced classification.
description
2014 nî lūn-bûn
@nan
2014 թուականի Սեպտեմբերին հրատարակուած գիտական յօդուած
@hyw
2014 թվականի սեպտեմբերին հրատարակված գիտական հոդված
@hy
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
name
nDNA-Prot: identification of DNA-binding proteins based on unbalanced classification.
@ast
nDNA-Prot: identification of DNA-binding proteins based on unbalanced classification.
@en
type
label
nDNA-Prot: identification of DNA-binding proteins based on unbalanced classification.
@ast
nDNA-Prot: identification of DNA-binding proteins based on unbalanced classification.
@en
prefLabel
nDNA-Prot: identification of DNA-binding proteins based on unbalanced classification.
@ast
nDNA-Prot: identification of DNA-binding proteins based on unbalanced classification.
@en
P2093
P2860
P356
P1433
P1476
nDNA-Prot: identification of DNA-binding proteins based on unbalanced classification.
@en
P2093
P2860
P2888
P356
10.1186/1471-2105-15-298
P50
P577
2014-09-08T00:00:00Z
P5875
P6179
1052165364