A machine learning pipeline for discriminant pathways identification

Motivation: Identifying the molecular pathways more prone to disruption during a pathological process is a key task in network medicine and, more in general, in systems biology. Results: In this work we propose a pipeline that couples a machine learning solution for molecular profiling with a recent network comparison method. The pipeline can identify changes occurring between specific sub-modules of networks built in a case-control biomarker study, discriminating key groups of genes whose interactions are modified by an underlying condition. The proposal is independent from the classification algorithm used. Three applications on genomewide data are presented regarding children susceptibility to air pollution and two neurodegenerative diseases: Parkinson's and Alzheimer's. Availability: Details about the software used for the experiments discussed in this paper are provided in the Appendix

25 Years of Self-organized Criticality: Concepts and Controversies

Introduced by the late Per Bak and his colleagues, self-organized criticality (SOC) has been one of the most stimulating concepts to come out of statistical mechanics and condensed matter theory in the last few decades, and has played a significant role in the development of complexity science. SOC, and more generally fractals and power laws, have attracted much comment, ranging from the very positive to the polemical. The other papers (Aschwanden et al. in Space Sci. Rev., 2014, this issue; McAteer et al. in Space Sci. Rev., 2015, this issue; Sharma et al. in Space Sci. Rev. 2015, in preparation) in this special issue showcase the considerable body of observations in solar, magnetospheric and fusion plasma inspired by the SOC idea, and expose the fertile role the new paradigm has played in approaches to modeling and understanding multiscale plasma instabilities. This very broad impact, and the necessary process of adapting a scientific hypothesis to the conditions of a given physical system, has meant that SOC as studied in these fields has sometimes differed significantly from the definition originally given by its creators. In Bak’s own field of theoretical physics there are significant observational and theoretical open questions, even 25 years on (Pruessner 2012). One aim of the present review is to address the dichotomy between the great reception SOC has received in some areas, and its shortcomings, as they became manifest in the controversies it triggered. Our article tries to clear up what we think are misunderstandings of SOC in fields more remote from its origins in statistical mechanics, condensed matter and dynamical systems by revisiting Bak, Tang and Wiesenfeld’s original papers

Interplay magnetism and temperature in the large-demensional limits of the Hubbard and t-J models

We describe a theory for the finite-temperature properties of the infinite dimensional (d"#infinity#) half-filled Hubbard model and the t-J model. The work presented extends the local moment approach (LMA) to include the effects of temperature. We start by investigating the t-J model, which provides the leading strong coupling asymptotics to the Hubbard model. The only relevant energy scale, apart from temperature T, is the cost to flip a spin in the exchange field of the neighbouring spins. By identifying the temperature dependence of the spin-flip cost w_p, we are able to obtain an exact solution to the d"#infinity# t-J model. For the Hubbard model we first consider a scenario where T enters solely implicitly via the thermal disorder of the local moment state, which we describe at unrestricted Hartree-Fock level. The low-energy scale for spin-flip excitations is identified via an RPA treatment of the transverse spin polarization propagator. Dynamical coupling of single-particle processes to the spin-excitations yields a renormalized self-consistent description of the self-energy. This is shown to be asymptotically exact in strong coupling and results describing the thermal evolution of the spectra are discussed for moderate to strong Interaction strengths. By translating the diagrams for the RPA-propagator and the self-energy into the imaginary-frequency formalism we include explicit temperature effects via Fermi functions. The imaginary-frequency expressions are then analytically continued to obtain the related retarded real-frequency expressions. We also derive the correct form of the dynamical conductivity on the d"#infinity# Bethe lattice. Results for finite-temperature single-particle spectra and conductivities are then presented and particular attention given to the thermal evolution of these quantities and the finite-temperature insulator to metal crossover. (author)SIGLEAvailable from British Library Document Supply Centre-DSC:D203926 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Growing Biochemical Networks: Identifying the Intrinsic Properties

SCOPUS: cp.kinfo:eu-repo/semantics/publishe