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This article deals with the identification of gene regulatory networks from experimental data using a statistical machine learning approach. A stochastic model of gene interactions capable of handling missing variables is proposed. It can be described as a dynamic Bayesian network particularly well suited to tackle the stochastic nature of gene regulation… (More)

We extend tree-based methods to the prediction of structured outputs using a kernelization of the algorithm that allows one to grow trees as soon as a kernel can be defined on the output space. The resulting algorithm, called output kernel trees (OK3), generalizes classification and regression trees as well as tree-based ensemble methods in a principled… (More)

In many discrimination problems a large amount of data is available but only a few of them are labeled. This provides a strong motivation to improve or develop methods for semi-supervised learning. In this paper, boosting is generalized to this task within the optimization framework of MarginBoost. We extend the margin definition to unlabeled data and… (More)

Link prediction is addressed as an output kernel learning task through semi-supervised Output Kernel Regression. Working in the framework of RKHS theory with vector-valued functions, we establish a new repre-senter theorem devoted to semi-supervised least square regression. We then apply it to get a new model (POKR: Penalized Output Kernel Regression) and… (More)

BACKGROUND
Elucidating biological networks between proteins appears nowadays as one of the most important challenges in systems biology. Computational approaches to this problem are important to complement high-throughput technologies and to help biologists in designing new experiments. In this work, we focus on the completion of a biological network from… (More)

A general framework is proposed for gradient boosting in supervised learning problems where the loss function is defined using a kernel over the output space. It extends boosting in a principled way to complex output spaces (images, text, graphs etc.) and can be applied to a general class of base learners working in kernelized output spaces. Empirical… (More)

MOTIVATION
Identifying the set of genes differentially expressed along time is an important task in two-sample time course experiments. Furthermore, estimating at which time periods the differential expression is present can provide additional insight into temporal gene functions. The current differential detection methods are designed to detect difference… (More)

MOTIVATION
Reverse engineering of gene regulatory networks remains a central challenge in computational systems biology, despite recent advances facilitated by benchmark in silico challenges that have aided in calibrating their performance. A number of approaches using either perturbation (knock-out) or wild-type time-series data have appeared in the… (More)

Devoted to multi-task learning and structured output learning, operator-valued kernels provide a flexible tool to build vector-valued functions in the context of Reproducing Kernel Hilbert Spaces. To scale up these methods, we extend the celebrated Random Fourier Feature methodology to get an approximation of operator-valued kernels. We propose a general… (More)

We consider the question of predicting nonlinear time series. Kernel Dy-namical Modeling (KDM), a new method based on kernels, is proposed as an extension to linear dynamical models. The kernel trick is used twice: first, to learn the parameters of the model, and second, to compute preimages of the time series predicted in the feature space by means of… (More)