Skip to main content


Browse the glossary using this index

Special | A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z | ALL



R is a programming environment and language with a focus on statistical analysis. R originated as a free software reimplementation of the S language with added support for static scoping.

Random Forest

A machine learning model that consists of an ensemble of ‘bagging’-type decision trees.


Refers to the task of predicting numerical values for new samples.

Regression model

A decision tree whose ‘leaves’ contain any model obtained by performing a regression technique on the data of that leaf (for example, a linear multi-variable model).

Regression tree

A decision tree with a constant among its ‘leaves’.


Technologies that allow the identification of RNA sequences in a cell sample as well as measurement of their abundance. In other words, the genes expressed in a sample at the time of the analysis can be identified and their degree of expression quantified. In addition to quantifying gene expression, the analysis of these data makes it possible to identify new sequences transcribed from DNA, identify alternative splicing mechanisms, or detect allele-specific expression, among others. Furthermore, these technologies allow the characterization not only of messenger RNA (mRNA), but also of other types of RNAs such as RNAs that do not encode proteins (referred to as non-coding RNAs, or ncRNAs), including lncRNAs and miRNAs, among others.


This is an integrated development environment or visual interface for the R programming language, dedicated to statistical computing and graphics.