Parea: Multi-view ensemble clustering for cancer subtype discovery.

Multi-view clustering methods are essential for the stratification of patients into sub-groups of similar molecular characteristics. In recent years, a wide range of methods have been developed for this purpose. However, due to the high diversity of cancer-related data, a single method may not perform sufficiently well in all cases.

Robust Random Forest-Based All-Relevant Feature Ranks for Trustworthy AI.

Feature selection is a fundamental challenge in machine learning. For instance in bioinformatics, it is essential when one wishes to detect biomarkers. Tree-based methods are predominantly used for this purpose.

A hierarchical clustering and data fusion approach for disease subtype discovery.

Recent advances in multi-omics clustering methods enable a more fine-tuned separation of cancer patients into clinical relevant clusters. These advancements have the potential to provide a deeper understanding of cancer progression and may facilitate the treatment of cancer patients. Here, we present a simple hierarchical clustering and data fusion approach, named HC-fused, for the detection of disease subtypes.

Genome scans for selection and introgression based on k-nearest neighbour techniques.

In recent years, genome-scan methods have been extensively used to detect local signatures of selection and introgression. Most of these methods are either designed for one or the other case, which may impair the study of combined cases. Here, we introduce a series of versatile genome-scan methods applicable for both cases, the detection of selection and introgression.

Publisher Correction: Inference of transcription factor binding from cell-free DNA enables tumor subtype prediction and early detection.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.