Fine-to-Coarse Ranking in Ordinal and Imbalanced Domains: An Application to Liver Transplantation

Nowadays imbalanced learning represents one of the most vividly discussed challenges in machine learning. In these scenarios, one or some of the classes in the problem have a significantly lower a priori probability, usually leading to trivial or non-desirable classifiers. Because of this, imbalanced learning has been researched to a great extent by means of different approaches. Recently, the focus has switched from binary classification to other paradigms where imbalanced data also arise, such as ordinal classification. This paper tests the application of learning pairwise ranking with multiple granularity levels in an ordinal and imbalanced classification problem where the aim is to construct an accurate model for donor-recipient allocation in liver transplantation. Our experiments show that approaching the problem as ranking solves the imbalance issue and leads to a competitive performance

Gut-Brain Axis: Role of Microbiota in Parkinson’s Disease and Multiple Sclerosis

It has recently been discovered that the digestive tract is lined with about 100 million nerve cells; the digestive tract has been baptized, metaphorically speaking, as “the second brain,” which contains a multitude of neurotransmitters, viruses, and bacteria that help regulate our emotional state. This second brain, known as the enteric nervous system, is a unique anatomical unit that extends from the esophagus to the anus. Like the nervous system, it produces a whole series of psychoactive substances, such as serotonin, dopamine, and opioids for pain, and synthesizes benzodiazepines. In it, we find the microbiota: a set of microorganisms (viruses and bacteria). Together with the brain, the microbiota directly influences mood, character, or sleep. Knowledge about the possible relationship of the microbiota with frequent neurological diseases is still just beginning. Recently, possible changes in the microbiota have been linked to the onset of Parkinson’s disease (PD). Also, today, we know that there are differences between the microbiota of healthy people and people with multiple sclerosis and that these differences have also been related to the disease and its evolution

Discovering hidden relationships between renal diseases and regulated genes through 3D network visualizations

Abstract Background In a recent study, two-dimensional (2D) network layouts were used to visualize and quantitatively analyze the relationship between chronic renal diseases and regulated genes. The results revealed complex relationships between disease type, gene specificity, and gene regulation type, which led to important insights about the underlying biological pathways. Here we describe an attempt to extend our understanding of these complex relationships by reanalyzing the data using three-dimensional (3D) network layouts, displayed through 2D and 3D viewing methods. Findings The 3D network layout (displayed through the 3D viewing method) revealed that genes implicated in many diseases (non-specific genes) tended to be predominantly down-regulated, whereas genes regulated in a few diseases (disease-specific genes) tended to be up-regulated. This new global relationship was quantitatively validated through comparison to 1000 random permutations of networks of the same size and distribution. Our new finding appeared to be the result of using specific features of the 3D viewing method to analyze the 3D renal network. Conclusions The global relationship between gene regulation and gene specificity is the first clue from human studies that there exist common mechanisms across several renal diseases, which suggest hypotheses for the underlying mechanisms. Furthermore, the study suggests hypotheses for why the 3D visualization helped to make salient a new regularity that was difficult to detect in 2D. Future research that tests these hypotheses should enable a more systematic understanding of when and how to use 3D network visualizations to reveal complex regularities in biological networks.http://deepblue.lib.umich.edu/bitstream/2027.42/112972/1/13104_2010_Article_700.pd

Transverse sphericity of primary charged particles in minimum bias proton-proton collisions at s=0.9\sqrt{s}=0.9, 2.76 and 7 TeV

Measurements of the sphericity of primary charged particles in minimum bias proton--proton collisions at $\sqrt{s}=0.9$, 2.76 and 7 TeV with the ALICE detector at the LHC are presented. The observable is linearized to be collinear safe and is measured in the plane perpendicular to the beam direction using primary charged tracks with $p_{\rm T}\geq0.5$ GeV/c in $|\eta|\leq0.8$. The mean sphericity as a function of the charged particle multiplicity at mid-rapidity ($N_{\rm ch}$) is reported for events with different $p_{\rm T}$ scales ("soft" and "hard") defined by the transverse momentum of the leading particle. In addition, the mean charged particle transverse momentum versus multiplicity is presented for the different event classes, and the sphericity distributions in bins of multiplicity are presented. The data are compared with calculations of standard Monte Carlo event generators. The transverse sphericity is found to grow with multiplicity at all collision energies, with a steeper rise at low $N_{\rm ch}$, whereas the event generators show the opposite tendency. The combined study of the sphericity and the mean $p_{\rm T}$ with multiplicity indicates that most of the tested event generators produce events with higher multiplicity by generating more back-to-back jets resulting in decreased sphericity (and isotropy). The PYTHIA6 generator with tune PERUGIA-2011 exhibits a noticeable improvement in describing the data, compared to the other tested generators.Comment: 21 pages, 9 captioned figures, 3 tables, authors from page 16, published version, figures from http://aliceinfo.cern.ch/ArtSubmission/node/308