PermutMatrix is a work space designed to graphically explore gene expression data. It relies on the graphical approach introduced by Eisen and also offers several methods for the optimal reorganization of rows and columns of a numerical dataset. For example, several methods are proposed for optimal reorganization of the leaves of a hierarchical clustering… (More)
BACKGROUND High-throughput sequencing technologies offer new perspectives for biomedical, agronomical and evolutionary research. Promising progresses now concern the application of these technologies to large-scale studies of genetic variation. Such studies require the genotyping of high numbers of samples. This is theoretically possible using 454… (More)
Abstmct-Probability inequalities are given for tbe deviation of the resubstitution error estimate from the unknown conditional probability of error. lle inequalities are distribution free and can be applied to linear diSCrhiMti0n rules, to nearest neighbor rules with a reduced sample size, and to b&gram rules.
, Inductive learning systems search for regularities that therefore be applied with some assurance to an example describe environmental observations, These systems often use which does not belong to the learning set. In other numeri~~l heu~stics to guide this search, The~ also sele~t words, statistical significance may be used to assess regulantles which… (More)
Two-dimensional gel electrophoresis, a routine application in proteomics, separates proteins according to their molecular mass (M(r)) and isoelectric point (pI). As the genomic sequences for more and more organisms are determined, the M(r) and pI of all their proteins can be estimated computationally. The examination of several of these theoretical proteome… (More)
We propose a novel BIST technique for non-scan sequential circuits which does not modify the circuit under test. It uses a learning algorithm to build a hardware test sequence generator capable of reproducing the essential features of a set of precomputed deterministic test sequences. We use for this purpose two new models called Hidden Markov Model with… (More)
We present a new model, derived from classical Hidden Mar-kov Models (HMMs), to learn sequences of large Boolean vectors. Our model – Hidden Markov Model with Patterns, or HMMP – differs from HMM by the fact that it uses patterns to define the emission probability distributions attached to the states. We also present an efficient state merging algorithm to… (More)