Pages that link to "Item:Q4547711"

The following pages link to Selection bias in gene extraction on the basis of microarray gene-expression data (Q4547711):

Displaying 50 items.

• Correlation and variable importance in random forests (Q58729) (← links)
• A network-based feature selection approach to identify metabolic signatures in disease (Q292792) (← links)
• Sparse regression and support recovery with \(\mathbb{L}_2\)-boosting algorithms (Q466526) (← links)
• Beyond support in two-stage variable selection (Q517395) (← links)
• A very fast algorithm for matrix factorization (Q544621) (← links)
• Selection bias in working with the top genes in supervised classification of tissue samples (Q713676) (← links)
• Sample size determination for training cancer classifiers from microarray and RNA-seq data (Q746699) (← links)
• Discrimination and scoring using small sets of genes for two-sample microarray data (Q876057) (← links)
• Gene selection and prediction for cancer classification using support vector machines with a reject option (Q901576) (← links)
• A multilevel tabu search algorithm for the feature selection problem in biomedical data (Q929176) (← links)
• Hybrid particle swarm optimization and tabu search approach for selecting genes for tumor classification using gene expression data (Q936035) (← links)
• On partial least squares dimension reduction for microarray-based classification: a simulation study (Q956938) (← links)
• Stable classification with applications to microarray data (Q957037) (← links)
• An extensive comparison of recent classification tools applied to microarray data (Q957169) (← links)
• Multiclass classification and gene selection with a stochastic algorithm (Q961825) (← links)
• Feature selection in omics prediction problems using cat scores and false nondiscovery rate control (Q977653) (← links)
• Variable selection in kernel Fisher discriminant analysis by means of recursive feature elimina\-tion (Q1010549) (← links)
• Application of a mixture model for determining the cutoff threshold for activity in high-throughput screening (Q1020044) (← links)
• Classification by ensembles from random partitions of high-dimensional data (Q1020719) (← links)
• Class prediction and gene selection for DNA microarrays using regularized sliced inverse regression (Q1020831) (← links)
• Stepwise feature selection using generalized logistic loss (Q1023708) (← links)
• Class prediction by nearest shrunken centroids, with applications to DNA microarrays. (Q1431225) (← links)
• Multiclass cancer classification by support vector machines with class-wise optimized genes and probability estimates (Q1624446) (← links)
• Functional data analysis in shape analysis (Q1658331) (← links)
• Multi-class clustering and prediction in the analysis of microarray data (Q1776767) (← links)
• Low rank updated LS-SVM classifiers for fast variable selection (Q1932006) (← links)
• High-dimensional variable selection via low-dimensional adaptive learning (Q2044323) (← links)
• Projective inference in high-dimensional problems: prediction and feature selection (Q2188473) (← links)
• Stability selection for Lasso, ridge and elastic net implemented with AFT models (Q2195264) (← links)
• Variable selection for binary classification in large dimensions: comparisons and application to microarray data (Q2197385) (← links)
• Gene selection via a new hybrid ant colony optimization algorithm for cancer classification in high-dimensional data (Q2283785) (← links)
• Bayesian variable selection in multinomial probit model for classifying high-dimensional data (Q2354735) (← links)
• Comparison of Bayesian predictive methods for model selection (Q2361448) (← links)
• Consensus analysis of multiple classifiers using non-repetitive variables: diagnostic application to microarray gene expression data (Q2373280) (← links)
• On selection biases with prediction rules formed from gene expression data (Q2475725) (← links)
• Multi-class tumor classification by discriminant partial least squares using microarray gene expression data and assessment of classification models (Q2490602) (← links)
• A Bayesian approach to nonlinear probit gene selection and classification (Q2492548) (← links)
• Exploiting scale-free information from expression data for cancer classification (Q2500327) (← links)
• Markov blanket-embedded genetic algorithm for gene selection (Q2643912) (← links)
• A new framework for identifying differentially expressed genes (Q2643913) (← links)
• A weight function method for selection of proteins to predict an outcome using protein expression data (Q2656115) (← links)
• Privacy preserving feature selection and multiclass classification for horizontally distributed data (Q2668550) (← links)
• Hybrid feature selection algorithm using symmetrical uncertainty and a harmony search algorithm (Q2798460) (← links)
• Estimating prediction error in microarray classification: modifications of the 0.632+ bootstrap when \(n<p\) (Q2852557) (← links)
• Comparing the characteristics of gene expression profiles derived by univariate and multivariate classification methods (Q2863956) (← links)
• Correcting the estimated level of differential expression for gene selection bias: application to a microarray study (Q2863960) (← links)
• Open-Set Nearest Shrunken Centroid Classification (Q2884866) (← links)
• Selecting Differentially Expressed Genes from Microarray Experiments (Q3079088) (← links)
• A method for constructing a confidence bound for the actual error rate of a prediction rule in high dimensions (Q3304962) (← links)
• An overview of recent developments in genomics and associated statistical methods (Q3559948) (← links)