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cran: batch
Batching Routines in Parallel and Passing Command-Line Arguments to RPapers that mentioned: 4
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bioconductor: HiCcompare
HiCcompare: Joint normalization and comparative analysis of multiple Hi-C datasetsPapers that mentioned: 4
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bioconductor: gwascat
representing and modeling data in the EMBL-EBI GWAS catalogPapers that mentioned: 4
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bioconductor: TADCompare
TADCompare: Identification and characterization of differential TADsPapers that mentioned: 4
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bioconductor: GOexpress
Visualise microarray and RNAseq data using gene ontology annotationsPapers that mentioned: 4
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cran: polyRAD
Genotype Calling with Uncertainty from Sequencing Data in Polyploids and DiploidsPapers that mentioned: 4
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bioconductor: XDE
XDE: a Bayesian hierarchical model for cross-study analysis of differential gene expressionPapers that mentioned: 4
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cran: ggalt
Extra Coordinate Systems, 'Geoms', Statistical Transformations, Scales and Fonts for 'ggplot2'Papers that mentioned: 4
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bioconductor: plgem
Detect differential expression in microarray and proteomics datasets with the Power Law Global Error Model (PLGEM)Papers that mentioned: 4
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cran: Metatron
Meta-analysis for Classification Data and Correction to Imperfect ReferencePapers that mentioned: 4
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cran: MCDM
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bioconductor: philr
Phylogenetic partitioning based ILR transform for metagenomics dataPapers that mentioned: 4
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cran: SpaDES
Develop and Run Spatially Explicit Discrete Event Simulation ModelsPapers that mentioned: 4
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cran: DiffNet
Detection of Statistically Significant Changes in Complex Biological NetworksPapers that mentioned: 4
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pypi: cuteSV
Long-read-based human genomic structural variation detection with cuteSVPapers that mentioned: 4
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cran: EMVS
The Expectation-Maximization Approach to Bayesian Variable SelectionPapers that mentioned: 3
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cran: expss
Tables, Labels and Some Useful Functions from Spreadsheets and 'SPSS' StatisticsPapers that mentioned: 3
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pypi: Curare
Curare: A Customizable and Reproducible Analysis Pipeline for RNA-Seq ExperimentsPapers that mentioned: 3
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pypi: KDVS
Knowledge Driven Variable Selection (KDVS) is an experimental knowledge extraction system that utilizes statistical learning and novel data and knowledge integration methodologies.Papers that mentioned: 3
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bioconductor: oncomix
Identifying Genes Overexpressed in Subsets of Tumors from Tumor-Normal mRNA Expression DataPapers that mentioned: 3
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cran: coefficientalpha
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cran: fastcox
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pypi: MorphoTester
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pypi: suit
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bioconductor: ExiMiR
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cran: eclust
Environment Based Clustering for Interpretable Predictive Models in High Dimensional DataPapers that mentioned: 3
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pypi: scikit-multiflow
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pypi: motifscan
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pypi: scikit-multilearn
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bioconductor: DEqMS
a tool to perform statistical analysis of differential protein expression for quantitative proteomics data.Papers that mentioned: 3
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