Differential analysis of biological networks

In cancer research, the comparison of gene expression or DNA methylation networks inferred from healthy controls and patients can lead to the discovery of biological pathways associated to the disease. As a cancer progresses, its signalling and control networks are subject to some degree of localised re-wiring. Being able to detect disrupted interaction patterns induced by the presence or progression of the disease can lead to the discovery of novel molecular diagnostic and prognostic signatures. Currently there is a lack of scalable statistical procedures for two-network comparisons aimed at detecting localised topological differences. We propose the dGHD algorithm, a methodology for detecting differential interaction patterns in two-network comparisons. The algorithm relies on a statistic, the Generalised Hamming Distance (GHD), for assessing the degree of topological difference between networks and evaluating its statistical significance. dGHD builds on a non-parametric permutation testing framework but achieves computationally efficiency through an asymptotic normal approximation. We show that the GHD is able to detect more subtle topological differences compared to a standard Hamming distance between networks. This results in the dGHD algorithm achieving high performance in simulation studies as measured by sensitivity and specificity. An application to the problem of detecting differential DNA co-methylation subnetworks associated to ovarian cancer demonstrates the potential benefits of the proposed methodology for discovering network-derived biomarkers associated with a trait of interest

A tree-based kernel for graphs with continuous attributes

The availability of graph data with node attributes that can be either discrete or real-valued is constantly increasing. While existing kernel methods are effective techniques for dealing with graphs having discrete node labels, their adaptation to non-discrete or continuous node attributes has been limited, mainly for computational issues. Recently, a few kernels especially tailored for this domain, and that trade predictive performance for computational efficiency, have been proposed. In this paper, we propose a graph kernel for complex and continuous nodes' attributes, whose features are tree structures extracted from specific graph visits. The kernel manages to keep the same complexity of state-of-the-art kernels while implicitly using a larger feature space. We further present an approximated variant of the kernel which reduces its complexity significantly. Experimental results obtained on six real-world datasets show that the kernel is the best performing one on most of them. Moreover, in most cases the approximated version reaches comparable performances to current state-of-the-art kernels in terms of classification accuracy while greatly shortening the running times.Comment: This work has been submitted to the IEEE Transactions on Neural Networks and Learning Systems for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

An Empirical Study on Budget-Aware Online Kernel Algorithms for Streams of Graphs

Kernel methods are considered an effective technique for on-line learning. Many approaches have been developed for compactly representing the dual solution of a kernel method when the problem imposes memory constraints. However, in literature no work is specifically tailored to streams of graphs. Motivated by the fact that the size of the feature space representation of many state-of-the-art graph kernels is relatively small and thus it is explicitly computable, we study whether executing kernel algorithms in the feature space can be more effective than the classical dual approach. We study three different algorithms and various strategies for managing the budget. Efficiency and efficacy of the proposed approaches are experimentally assessed on relatively large graph streams exhibiting concept drift. It turns out that, when strict memory budget constraints have to be enforced, working in feature space, given the current state of the art on graph kernels, is more than a viable alternative to dual approaches, both in terms of speed and classification performance.Comment: Author's version of the manuscript, to appear in Neurocomputing (ELSEVIER

Innovazione e rete come chiavi del rilancio dei distretti

Associazione per lo Sviluppo del Distretto della Sedia (ASDI Sedia

Relativistic Planck-scale polymer

Polymer quantum mechanics has been studied as a simplified picture that reflects some of the key properties of Loop Quantum Gravity; however, while the fate of relativistic symmetries in Loop Quantum Gravity is still not established, it is usually assumed that the discrete polymer structure should lead to a breakdown of relativistic symmetries. We here focus for simplicity on a one-spatial-dimension polymer model and show that relativistic symmetries are deformed, rather than being broken. The specific type of deformed relativistic symmetries which we uncover appears to be closely related to analogous descriptions of relativistic symmetries in some noncommutative spacetimes. This also contributes to an ongoing effort attempting to establish whether the "quantum-Minkowski limit" of Loop Quantum Gravity is a noncommutative spacetime.Comment: 5 pages, no figures. v2: minor changes in Section I

Is Vivaldi smooth and takete? Non-verbal sensory scales for describing music qualities

Studies on the perception of music qualities (such as induced or perceived emotions, performance styles, or timbre nuances) make a large use of verbal descriptors. Although many authors noted that particular music qualities can hardly be described by means of verbal labels, few studies have tried alternatives. This paper aims at exploring the use of non-verbal sensory scales, in order to represent different perceived qualities in Western classical music. Musically trained and untrained listeners were required to listen to six musical excerpts in major key and to evaluate them from a sensorial and semantic point of view (Experiment 1). The same design (Experiment 2) was conducted using musically trained and untrained listeners who were required to listen to six musical excerpts in minor key. The overall findings indicate that subjects\u2019 ratings on non-verbal sensory scales are consistent throughout and the results support the hypothesis that sensory scales can convey some specific sensations that cannot be described verbally, offering interesting insights to deepen our knowledge on the relationship between music and other sensorial experiences. Such research can foster interesting applications in the field of music information retrieval and timbre spaces explorations together with experiments applied to different musical cultures and contexts

Mozart is still blue: a comparison of sensory and verbal scales to describe qualities in music

An experiment was carried out in order to assess the use of non-verbal sensory scales for evaluating perceived music qualities, by comparing them with the analogous verbal scales. Participants were divided into two groups; one group (SV) completed a set of non-verbal scales responses and then a set of verbal scales responses to short musical extracts. A second group (VS) completed the experiment in the reverse order. Our hypothesis was that the ratings of the SV group can provide information unmediated (or less mediated) by verbal association in a much stronger way than the VS group. Factor analysis performed separately on the SV group, the VS group and for all participants shows a recurring patterning of the majority of sensory scales versus the verbal scales into different factors. Such results suggest that the sensory scale items are indicative of a different semantic structure than the verbal scales in describing music, and so they are indexing different qualities (perhaps ineffable), making them potentially special contributors to understanding musical experience

Missa Assumpta est Maria

Mass setting by Giovanni Pierluigi da Palestrina in honor of the Assumption of Mary. This mass setting was arranged for a mixed chorus (two sopranos, alto, two tenors, and bass voice parts) by Franz X. Haberl.https://ecommons.udayton.edu/imri_sheetmusic/1076/thumbnail.jp