SLIDER: Mining correlated motifs in protein-protein interaction networks

Abstract—Correlated motif mining (CMM) is the problem to find overrepresented pairs of patterns, called motif pairs, in interacting protein sequences. Algorithmic solutions for CMM thereby provide a computational method for predicting binding sites for protein interaction. In this paper, we adopt a motif-driven approach where the support of candidate motif pairs is evaluated in the network. We experimentally establish the superiority of the Chi-square-based support measure over other support measures. Furthermore, we obtain that CMM is an NP-hard problem for a large class of support measures (including Chi-square) and reformulate the search for correlated motifs as a combinatorial optimization problem. We then present the method SLIDER which uses local search with a neighborhood function based on sliding motifs and employs the Chi-square-based support measure. We show that SLIDER outperforms existing motif-driven CMM methods and scales to large protein-protein interaction networks

Evaluating real-time forecasts in real-time

The accuracy of real-time forecasts of macroeconomicvariables that are subject to revisions may crucially depend on thechoice of data used to compare the forecasts against. We put forwarda flexible time-varying parameter regression framework to obtainearly estimates of the final value of macroeconomic variables basedupon the initial data release that may be used as actuals in currentforecast evaluation. We allow for structural changes in theregression parameters to accommodate benchmark revisions anddefinitional changes, which fundamentally change the statisticalproperties of the variable of interest, including the relationshipbetween the final value and the initial release. The usefulness ofour approach is demonstrated through an empirical applicationcomparing the accuracy of forecasts of US GDP growth rates from theSurvey of Professional Forecasters and the Greenbook.forecast evaluation;Bayesian estimation;structural breaks;data revision;parameter uncertainty

Predictive gains from forecast combinations using time-varying model weights

Several frequentist and Bayesian model averaging schemes, including a new one that simultaneously allows for parameter uncertainty, model uncertainty and time varying model weights, are compared in terms of forecast accuracy over a set of simulation experiments. Artificial data are generated, characterized by low predictability, structural instability, and fat tails, which is typical for many financial-economic time series. Sensitivity of results with respect to misspecification of the number of included predictors and the number of included models is explored. Given the set up of our experiments, time varying model weight schemes outperform other averaging schemes in terms of predictive gains both when the correlation among individual forecasts is low and the underlying data generating process is subject to structural locations shifts. In an empirical application using returns on the S&P 500 index, time varying model weights provide improved forecasts with substantial economic gains in an investment strategy including transaction costs.Bayesian model averaging;forecast combination;stock return predictability;time-varying weight combination

Ultrafast spectroscopy of single molecules

We present a single-molecule study on femtosecond dynamics in multichromophoric systems, combining fs pump-probe, emission-spectra and fluorescence-lifetime analysis. At the single molecule level a wide range of exciton delocalisation lengths and energy redistribution times is revealed. Next, two color pump-probe experiments are presented as a step to addressing ultrafast energy transfer in individual complexes

Simulation based Bayesian econometric inference: principles and some recent computational advances

In this paper we discuss several aspects of simulation based Bayesian econometric inference. We start at an elementary level on basic concepts of Bayesian analysis; evaluating integrals by simulation methods is a crucial ingredient in Bayesian inference. Next, the most popular and well-known simulation techniques are discussed, the MetropolisHastings algorithm and Gibbs sampling (being the most popular Markov chain Monte Carlo methods) and importance sampling. After that, we discuss two recently developed sampling methods: adaptive radial based direction sampling [ARDS], which makes use of a transformation to radial coordinates, and neural network sampling, which makes use of a neural network approximation to the posterior distribution of interest. Both methods are especially useful in cases where the posterior distribution is not well-behaved, in the sense of having highly non-elliptical shapes. The simulation techniques are illustrated in several example models, such as a model for the real US GNP and models for binary data of a US recession indicator.

Adaptive Mixture of Student-t Distributions as a Flexible Candidate Distribution for Efficient Simulation: The R Package AdMit

This paper presents the R package AdMit which provides flexible functions to approximate a certain target distribution and to efficiently generate a sample of random draws from it, given only a kernel of the target density function. The core algorithm consists of the function AdMit which fits an adaptive mixture of Student-t distributions to the density of interest. Then, importance sampling or the independence chain Metropolis-Hastings algorithm is used to obtain quantities of interest for the target density, using the fitted mixture as the importance or candidate density. The estimation procedure is fully automatic and thus avoids the time-consuming and difficult task of tuning a sampling algorithm. The relevance of the package is shown in two examples. The first aims at illustrating in detail the use of the functions provided by the package in a bivariate bimodal distribution. The second shows the relevance of the adaptive mixture procedure through the Bayesian estimation of a mixture of ARCH model fitted to foreign exchange log-returns data. The methodology is compared to standard cases of importance sampling and the Metropolis-Hastings algorithm using a naive candidate and with the Griddy-Gibbs approach.

A local view on single and coupled molecules

The paper focuses on a novel approach to reveal ultrafast dynamics in single molecules. The main strength of the approach is towards ultrafast processes in extended multi-chromophoric molecular assemblies. Excitonically coupled systems consisting of 2 and 3 rigidly linked perylene-diimide units in a head to tail configuration are studied. Superradiance and inhibited intramolecular decay are observed and discrete jumps in femtosecond response upon break-up of the strong coupling are revealed