Bioinformatics and Computational Biology Solutions Using R and Bioconductor

By J. Davis. 2007

2,235 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/d0da9ce3ca989bce2579b64be9aed518265a8994

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

Citations in the corpus

ArrayExpress update—from an archive of functional genomics experiments to the atlas of gene expression (2008, methodology): “The Atlas data content is produced using a meta-analytical approach which uses the Bioconductor module limma (7).”
Silhouette Analysis for Performance Evaluation in Machine Learning with Applications to Clustering (2021, background): “A search strategy is required to ﬁnd the groups that optimize the objective function [1].”
Ten quick tips for machine learning in computational biology (2017, methodology): “It provides several libraries for machine learning algorithms (including, for example, k -nearest neighbors and k -means), effective libraries for statistical visualization (such as ggplot2 [50]), and statistical analysis packages (such as the extremely popular Bioconductor package [51]).”
Depletion of Carcinoma-Associated Fibroblasts and Fibrosis Induces Immunosuppression and Accelerates Pancreas Cancer with Diminished Survival (2014, positive): “Gene expression analysis was performed using Mouse Ref6 Gene Expression BeadChip (Illumina), and the Limma package from R Bioconductor (Carey et al., 2005) was used to analyze differentially expressed genes of myofibroblast-depleted mice versus control mice (p % 0.05, fold change R 1.5).”
Integrating diverse genomic data using gene sets (2011, methodology): “We perform gene set analysis on all data types separately, using a standard methodology implemented in the limma package in Bioconductor [5,30].”
Perturbation-response genes reveal signaling footprints in cancer gene expression (2016, methodology): “…all single-channel microarrays with at least duplicates in the basal condition with raw data available that could be processed by either the limma (Smyth 2005), oligo(Carvalho and Irizarry 2010), or affy (Gautier et al. 2004) BioConductor packages and for which there was a respective annotation…”
A data-driven approach to preprocessing Illumina 450K methylation array data (2013, methodology): “The functions in the package are all generic and methods are provided for the objects produced by the packages methylumi [18], minfi [19], and IMA [20], as well as wrappers for the methods of Dedeurwaerder et al [5] A number of packages from CRAN and bioConductor were used: quantile normalization us”