Random forests (RF) is a popular tree-based ensemble machine learning tool that is highly
data adaptive, applies to “large p, small n” problems, and is able to account for correlation as …

Ensemble learning is an intensively studied technique in machine learning and pattern
recognition. Recent work in computational biology has seen an increasing use of ensemble …

Aims: The study aims to find a way to reduce the dimensionality of the dataset. Background:
Dimensionality reduction is the key issue of the machine learning process. It does not only …

Background Many functional analysis tools have been developed to extract functional and
mechanistic insight from bulk transcriptome data. With the advent of single-cell RNA …

Abstract In the Life Sciences 'omics' data is increasingly generated by different high-
throughput technologies. Often only the integration of these data allows uncovering …

Nonalcoholic steatohepatitis (NASH) is a condition which can progress to cirrhosis and
hepatocellular carcinoma. Markers for NASH diagnosis are still lacking. We performed a …

The analysis of large genomic data is hampered by issues such as a small number of
observations and a large number of predictive variables (commonly known as “large P small …

Background Regression is a common statistical tool for prediction in neuroscience.
However, linear regression is by far the most common form of regression used, with …

Tumour metastasis suppressors are inhibitors of metastasis but their mechanisms of action
are generally not understood. We previously showed that the suppressor Raf kinase …

Saccadic (rapid) eye movements are primary means by which humans and non-human
primates sample visual information. However, while saccadic decisions are intensively …