Pierre Dupont

Learn More
MOTIVATION Biomarker discovery is an important topic in biomedical applications of computational biology, including applications such as gene and SNP selection from high-dimensional data. Surprisingly, the stability with respect to sampling variation or robustness of such selection processes has received attention only recently. However, robustness of(More)
This work presents a novel procedure for computing (1) distances between nodes of a weighted, undirected, graph, called the Euclidean Commute Time Distance (ECTD), and (2) a subspace projection of the nodes of the graph that preserves as much variance as possible, in terms of the ECTD – a principal components analysis of the graph. It is based on a(More)
In this paper, we extend the characterization of the search space of regular inference DMV94] to sequential presentations of learning data. We propose the RPNI2 algorithm, an incremental extension of the RPNI algorithm. We study the convergence and complexities of both algorithms from a theoretical and practical point of view. These results are assessed on(More)
This article presents an overview of Probabilistic Automata (PA) and discrete Hidden Markov Models (HMMs), and aims at clarifying the links between them. The first part of this work concentrates on probability distributions generated by these models. Necessary and sufficient conditions for an automaton to define a probabilistic language are detailed. It is(More)
& This article presents a novel application of grammatical inference techniques to the synthesis of behavior models of software systems. This synthesis is used for the elicitation of software requirements. This problem is formulated as a deterministic finite-state automaton induction problem from positive and negative scenarios provided by an end user of(More)
In an increasing number of domains such as bioinformatics, combinatorial graph problems arise. We propose a novel way to solve these problems, mainly those that can be translated to constrained subgraph finding. Our approach extends constraint programming by introducing CP(Graph), a new computation domain focused on graphs including a new type of variable:(More)
Requirements-related scenarios capture typical examples of system behaviors through sequences of desired interactions between the software-to-be and its environment. Their concrete, narrative style of expression makes them very effective for eliciting software requirements and for validating behavior models. However, scenarios raise coverage problems as(More)
We recall brieey in this paper the formal theory of regular grammatical inference from positive and negative samples of the language to be learned. We state this problem as a search toward an optimal element in a boolean lattice built from the positive information. We explain how a genetic search technique may be applied to this problem and we introduce a(More)
MOTIVATION Subgraph extraction is a powerful technique to predict pathways from biological networks and a set of query items (e.g. genes, proteins, compounds, etc.). It can be applied to a variety of different data types, such as gene expression, protein levels, operons or phylogenetic profiles. In this article, we investigate different approaches to(More)