Hierarchical information clustering by means of topologically embedded graphs

We introduce a graph-theoretic approach to extract clusters and hierarchies in complex data-sets in an unsupervised and deterministic manner, without the use of any prior information. This is achieved by building topologically embedded networks containing the subset of most significant links and analyzing the network structure. For a planar embedding, this method provides both the intra-cluster hierarchy, which describes the way clusters are composed, and the inter-cluster hierarchy which describes how clusters gather together. We discuss performance, robustness and reliability of this method by first investigating several artificial data-sets, finding that it can outperform significantly other established approaches. Then we show that our method can successfully differentiate meaningful clusters and hierarchies in a variety of real data-sets. In particular, we find that the application to gene expression patterns of lymphoma samples uncovers biologically significant groups of genes which play key-roles in diagnosis, prognosis and treatment of some of the most relevant human lymphoid malignancies.Comment: 33 Pages, 18 Figures, 5 Table

Therapeutic decision-making for patients with fluctuating mitral regurgitation

Mitral regurgitation (MR) is a common, progressive, and difficult-to-manage disease. MR is dynamic in nature, with physiological fluctuations occurring in response to various stimuli such as exercise and ischaemia, which can precipitate the development of symptoms and subsequent cardiac events. In both chronic primary and secondary MR, the dynamic behaviour of MR can be reliably examined during stress echocardiography. Dynamic fluctuation of MR can also have prognostic value; patients with a marked increase in regurgitant volume or who exhibit increased systolic pulmonary artery pressure during exercise have lower symptom-free survival than those who do not experience significant changes in MR and systolic pulmonary artery pressure during exercise. Identifying patients who have dynamic MR, and understanding the mechanisms underlying the condition, can potentially influence revascularization strategies (such as the surgical restoration of coronary blood flow) and interventional treatment (including cardiac resynchronization therapy and new approaches targeted to the mitral valve)