Showing 5 of total 5 results (show query)
bioc
hypeR:An R Package For Geneset Enrichment Workflows
An R Package for Geneset Enrichment Workflows.
Maintained by Anthony Federico. Last updated 5 months ago.
genesetenrichmentannotationpathwaysbioinformaticscomputational-biologygeneset-enrichment-analysis
76 stars 8.22 score 145 scripts
swsoyee
r3dmol:Create Interactive 3D Visualizations of Molecular Data
Create rich and fully interactive 3D visualizations of molecular data. Visualizations can be included in Shiny apps and R markdown documents, or viewed from the R console and 'RStudio' Viewer. 'r3dmol' includes an extensive API to manipulate the visualization after creation, and supports getting data out of the visualization into R. Based on the '3dmol.js' and the 'htmlwidgets' R package.
Maintained by Wei Su. Last updated 1 years ago.
3dcomputational-biologycomputational-chemistryhacktoberfesthtmlwidgetsmolecular-graphicsmolecular-modelingproteinprotein-structurevisualization
90 stars 6.35 score 166 scripts 1 dependentsbioc
gemini:GEMINI: Variational inference approach to infer genetic interactions from pairwise CRISPR screens
GEMINI uses log-fold changes to model sample-dependent and independent effects, and uses a variational Bayes approach to infer these effects. The inferred effects are used to score and identify genetic interactions, such as lethality and recovery. More details can be found in Zamanighomi et al. 2019 (in press).
Maintained by Sidharth Jain. Last updated 5 months ago.
softwarecrisprbayesiandataimportcomputational-biologygenetic-interactions
15 stars 6.02 score 9 scriptsbioc
biotmle:Targeted Learning with Moderated Statistics for Biomarker Discovery
Tools for differential expression biomarker discovery based on microarray and next-generation sequencing data that leverage efficient semiparametric estimators of the average treatment effect for variable importance analysis. Estimation and inference of the (marginal) average treatment effects of potential biomarkers are computed by targeted minimum loss-based estimation, with joint, stable inference constructed across all biomarkers using a generalization of moderated statistics for use with the estimated efficient influence function. The procedure accommodates the use of ensemble machine learning for the estimation of nuisance functions.
Maintained by Nima Hejazi. Last updated 5 months ago.
regressiongeneexpressiondifferentialexpressionsequencingmicroarrayrnaseqimmunooncologybioconductorbioconductor-packagebioconductor-packagesbioinformaticsbiomarker-discoverybiostatisticscausal-inferencecomputational-biologymachine-learningstatisticstargeted-learning
5 stars 5.30 score 5 scriptsbioc
geneXtendeR:Optimized Functional Annotation Of ChIP-seq Data
geneXtendeR optimizes the functional annotation of ChIP-seq peaks by exploring relative differences in annotating ChIP-seq peak sets to variable-length gene bodies. In contrast to prior techniques, geneXtendeR considers peak annotations beyond just the closest gene, allowing users to see peak summary statistics for the first-closest gene, second-closest gene, ..., n-closest gene whilst ranking the output according to biologically relevant events and iteratively comparing the fidelity of peak-to-gene overlap across a user-defined range of upstream and downstream extensions on the original boundaries of each gene's coordinates. Since different ChIP-seq peak callers produce different differentially enriched peaks with a large variance in peak length distribution and total peak count, annotating peak lists with their nearest genes can often be a noisy process. As such, the goal of geneXtendeR is to robustly link differentially enriched peaks with their respective genes, thereby aiding experimental follow-up and validation in designing primers for a set of prospective gene candidates during qPCR.
Maintained by Bohdan Khomtchouk. Last updated 5 months ago.
chipseqgeneticsannotationgenomeannotationdifferentialpeakcallingcoveragepeakdetectionchiponchiphistonemodificationdataimportnaturallanguageprocessingvisualizationgosoftwarebioconductorbioinformaticscchip-seqcomputational-biologyepigeneticsfunctional-annotation
9 stars 3.95 score 5 scripts