Single-cell RNA-seq has emerged as a powerful tool in diverse applications, from determining the cell-type composition of tissues to uncovering regulators of developmental programs. A near-universal step...
Read More »Classification of tumor types with gene expression data
The Cancer Genome Atlas (TCGA) has generated comprehensive molecular profiles. Researchers at the National Institute of Environmental Health Sciences aimed to identify a set of genes whose expression patterns can distinguish diverse tumor types. Those features may serve as biomarkers ...
Read More »MINT – a multivariate integrative method to identify reproducible molecular signatures across independent experiments and platforms
Molecular signatures identified from high-throughput transcriptomic studies often have poor reliability and fail to reproduce across studies. One solution is to combine independent studies into a single integrative analysis, additionally increasing sample size. However, the different protocols and technological platforms ...
Read More »GOexpress – an R/Bioconductor package for the identification and visualisation of robust gene ontology signatures through supervised learning of gene expression data
Identification of gene expression profiles that differentiate experimental groups is critical for discovery and analysis of key molecular pathways and also for selection of robust diagnostic or prognostic biomarkers. While integration of differential expression statistics has been used to refine ...
Read More »miRNEST 2.0 – a database of plant and animal microRNAs
Ever growing interest in microRNAs has immensely populated the number of resources and research papers devoted to the field and, as a result, it becomes more and more demanding to find miRNA data of interest. To mitigate this problem, researchers ...
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