UFFizi: a generic platform for ranking informative features.

Background: Feature selection is an important pre-processing task in the analysis of complex data. Selecting an appropriate subset of features can improve classification or clustering and lead to better understanding of the data. An important example is that of finding an informative group of genes out of thousands that appear in gene-expression analysis.

Global considerations in hierarchical clustering reveal meaningful patterns in data.

Background: A hierarchy, characterized by tree-like relationships, is a natural method of organizing data in various domains. When considering an unsupervised machine learning routine, such as clustering, a bottom-up hierarchical (BU, agglomerative) algorithm is used as a default and is often the only method applied.

Methodology/principal Findings: We show that hierarchical clustering that involve global considerations, such as top-down (TD, divisive), or glocal (global-local) algorithms are better suited to reveal meaningful patterns in the data.

Unsupervised feature selection under perturbations: meeting the challenges of biological data.

Motivation: Feature selection methods aim to reduce the complexity of data and to uncover the most relevant biological variables. In reality, information in biological datasets is often incomplete as a result of untrustworthy samples and missing values. The reliability of selection methods may therefore be questioned.

Novel unsupervised feature filtering of biological data.

Motivation: Many methods have been developed for selecting small informative feature subsets in large noisy data. However, unsupervised methods are scarce. Examples are using the variance of data collected for each feature, or the projection of the feature on the first principal component.