AnA/B-Testevaluates the impact of a new technology by running it in a real production environment and testing its performance on a set of items. Recent development efforts aroundA/B-Testsrevolve around dynamic allocation. They allow for quicker determination of the best variation (A or B), thus saving money for the user. However, dynamic allocation by traditional methods requires certain assumptions, which are not always valid in reality. This is often due to the fact that the populations being tested are not homogeneous. This article reports on a new reinforcement learning methodology which has been deployed by the commercialA/B-Testplatform AB Tasty. We provide a new method that not only builds homogeneous groups of users, but also allows the best variation for these groups to be found in a short period of time. This article provides numerical results on AB Tasty's data, in addition to public datasets, tha demonstrate an improvement over traditional methods.

MOTIVATION: With the growth of big data, variable selection has become one of the critical challenges in statistics. Although many methods have been proposed in the literature, their performance in terms of recall (sensitivity) and precision (predictive positive value) is limited in a context where the number of variables by far exceeds the number of observations or in a highly correlated setting. RESULTS: In this article, we propose a general algorithm, which improves the precision of any existing variable selection method. This algorithm is based on highly intensive simulations and takes into account the correlation structure of the data. Our algorithm can either produce a confidence index for variable selection or be used in an experimental design planning perspective. We demonstrate the performance of our algorithm on both simulated and real data. We then apply it in two different ways to improve biological network reverse-engineering. AVAILABILITY AND IMPLEMENTATION: Code is available as the SelectBoost package on the CRAN, https://cran.r-project.org/package=SelectBoost. Some network reverse-engineering functionalities are available in the Patterns CRAN package, https://cran.r-project.org/package=Patterns. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: A vast literature from the past decade is devoted to relating gene profiles and subject survival or time to cancer recurrence. Biomarker discovery from high-dimensional data, such as transcriptomic or single nucleotide polymorphism profiles, is a major challenge in the search for more precise diagnoses. The proportional hazard regression model suggested by Cox (1972), to study the relationship between the time to event and a set of covariates in the presence of censoring is the most commonly used model for the analysis of survival data. However, like multivariate regression, it supposes that more observations than variables, complete data, and not strongly correlated variables are available. In practice, when dealing with high-dimensional data, these constraints are crippling. Collinearity gives rise to issues of over-fitting and model misidentification. Variable selection can improve the estimation accuracy by effectively identifying the subset of relevant predictors and enhance the model interpretability with parsimonious representation. To deal with both collinearity and variable selection issues, many methods based on least absolute shrinkage and selection operator penalized Cox proportional hazards have been proposed since the reference paper of Tibshirani. Regularization could also be performed using dimension reduction as is the case with partial least squares (PLS) regression. We propose two original algorithms named sPLSDR and its non-linear kernel counterpart DKsPLSDR, by using sparse PLS regression (sPLS) based on deviance residuals. We compared their predicting performance with state-of-the-art algorithms on both simulated and real reference benchmark datasets. RESULTS: sPLSDR and DKsPLSDR compare favorably with other methods in their computational time, prediction and selectivity, as indicated by results based on benchmark datasets. Moreover, in the framework of PLS regression, they feature other useful tools, including biplots representation, or the ability to deal with missing data. Therefore, we view them as a useful addition to the toolbox of estimation and prediction methods for the widely used Cox's model in the high-dimensional and low-sample size settings. AVAILABILITY AND IMPLEMENTATION: The R-package plsRcox is available on the CRAN and is maintained by Frédéric Bertrand. http://cran.r-project.org/web/packages/plsRcox/index.html. CONTACT: [email protected] or [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: Temporal gene interactions, in response to environmental stress, form a complex system that can be efficiently described using gene regulatory networks. They allow highlighting the more influential genes and spotting some targets for biological intervention experiments. Despite that many reverse engineering tools have been designed, the Cascade package is an integrated solution adding several new and original key features such as the ability to predict changes in gene expressions after a biological perturbation in the network and graphical outputs that allow monitoring the spread of a signal through the network. AVAILABILITY AND IMPLEMENTATION: The R package Cascade is available online at http://www-math.u-strasbg.fr/genpred/spip.php?rubrique4.