The following pages link to Bioconductor (Q16389):
Displaying 50 items.
- Test-statistic correlation and data-row correlation (Q2216977) (← links)
- Identifying atypically expressed chromosome regions using RNA-Seq data (Q2220294) (← links)
- Bayesian sparse multivariate regression with asymmetric nonlocal priors for microbiome data analysis (Q2226696) (← links)
- Model-based feature selection and clustering of RNA-seq data for unsupervised subtype discovery (Q2233190) (← links)
- An empirical Bayes change-point model for transcriptome time-course data (Q2233191) (← links)
- Statistical analysis of next generation sequencing data (Q2250275) (← links)
- Partial least squares classification for high dimensional data using the PCOUT algorithm (Q2255855) (← links)
- A Bayesian mixture model for chromatin interaction data (Q2258449) (← links)
- RNA bioinformatics (Q2259130) (← links)
- minPtest: a resampling based gene region-level testing procedure for genetic case-control studies (Q2259695) (← links)
- Multiclass classification of sarcomas using pathway based feature selection method (Q2260281) (← links)
- A mixture factor model with applications to microarray data (Q2273145) (← links)
- Nonparametric false discovery rate control for identifying simultaneous signals (Q2286361) (← links)
- Model simplification for supervised classification of metabolic networks (Q2294595) (← links)
- Projected tests for high-dimensional covariance matrices (Q2301103) (← links)
- Large-scale local causal inference of gene regulatory relationships (Q2302806) (← links)
- Adaptive gPCA: a method for structured dimensionality reduction with applications to microbiome data (Q2318673) (← links)
- Assessing genome-wide significance for the detection of differentially methylated regions (Q2324944) (← links)
- \texttt{MLML2R}: an R package for maximum likelihood estimation of DNA methylation and hydroxymethylation proportions (Q2324967) (← links)
- Combining gene expression data and prior knowledge for inferring gene regulatory networks via Bayesian networks using structural restrictions (Q2324978) (← links)
- Dynamic adaptive procedures that control the false discovery rate (Q2326045) (← links)
- A novel matched-pairs feature selection method considering with tumor purity for differential gene expression analyses (Q2328387) (← links)
- Data science vs. statistics: two cultures? (Q2329839) (← links)
- General power and sample size calculations for high-dimensional genomic data (Q2344242) (← links)
- Simple estimators of false discovery rates given as few as one or two \(p\)-values without strong parametric assumptions (Q2344250) (← links)
- A multi-functional analyzer uses parameter constraints to improve the efficiency of model-based gene-set analysis (Q2349566) (← links)
- Hierarchical Bayes variable selection and microarray experiments (Q2370539) (← links)
- Multi-group cancer outlier differential gene expression detection (Q2373283) (← links)
- A two-sample test for high-dimensional data with applications to gene-set testing (Q2380090) (← links)
- No counts, no variance: allowing for loss of degrees of freedom when assessing biological variability from RNA-seq data (Q2406178) (← links)
- Comparing the performance of linear and nonlinear principal components in the context of high-dimensional genomic data integration (Q2406189) (← links)
- A graph Laplacian prior for Bayesian variable selection and grouping (Q2416741) (← links)
- Statistical methods for the analysis of high-throughput data based on functional profiles derived from the Gene Ontology (Q2455419) (← links)
- Multiple testing procedures with applications to genomics. (Q2470259) (← links)
- R Version 2.1.0 (Q2488392) (← links)
- Predicting \(O\)-glycosylation sites in mammalian proteins by using SVMs (Q2500380) (← links)
- Empirical study for the agreement between statistical methods in quality assessment and control of microarray data (Q2513345) (← links)
- Prospects and challenges in R package development (Q2513355) (← links)
- Reproducible statistical analysis with multiple languages (Q2513357) (← links)
- Asymptotic normality for inference on multisample, high-dimensional mean vectors under mild conditions (Q2516392) (← links)
- Random forests in count data modelling: an analysis of the influence of data features and overdispersion on regression performance (Q2684565) (← links)
- Modeling cell populations measured by flow cytometry with covariates using sparse mixture of regressions (Q2686038) (← links)
- Pathway analysis for RNA-seq data using a score-based approach (Q2805192) (← links)
- Modeling overdispersion heterogeneity in differential expression analysis using mixtures (Q2827191) (← links)
- Simulating the physiology of athletes during endurance sports events: modelling human energy conversion and metabolism (Q2889097) (← links)
- An empirical Bayesian approach for identifying differential coexpression in high-throughput experiments (Q2912340) (← links)
- Sample size calculations for designing clinical proteomic profiling studies using mass spectrometry (Q2921247) (← links)
- Rotation gene set testing for longitudinal expression data (Q2931062) (← links)
- The Impact of Measurement Error on Principal Component Analysis (Q2932773) (← links)
- Gene Expression Array Exploration Using $\mathcal{K}$ -Formal Concept Analysis (Q3003414) (← links)
