Genome-wide association studies (GWASs) have become the focus of the statistical analysis
of complex traits in humans, successfully shedding light on several aspects of genetic …

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 …

Compared to univariate analysis of genome-wide association (GWA) studies, machine
learning–based models have been shown to provide improved means of learning such …

Feature selection enhances classification accuracy by removing irrelevant and redundant
feature. Feature selection plays an important role in data mining and pattern recognition. In …

Background Feature selection is an essential task in single-cell RNA-seq (scRNA-seq) data
analysis and can be critical for gene dimension reduction and downstream analyses, such …

Ensemble classification is a well-established approach that involves fusing the decisions of
multiple predictive models. A similar “ensemble logic” has been recently applied to …

Feature selection is an important step in data mining and is used in various domains
including genetics, medicine, and bioinformatics. Choosing the important features (genes) is …

Due to the rapid increase in the availability of patient data, there is significant interest in
precision medicine that could facilitate the development of a personalized treatment plan for …

A digital polymerase chain reaction (dPCR) using fluorescence images for collecting
quantitative information needs efficient software tools to automate the image analysis …

Recently, there has been a growing interest in the development of pharmacological
interventions targeting ageing, as well as in the use of machine learning for analysing …