We introduce Bayesian sensing hidden Markov models (BS-HMMs) to represent speech data based on a set of state-dependent basis vectors. By incorporating the prior density of sensin...
We present BayesMD, a Bayesian Motif Discovery model with several new features. Three different types of biological a priori knowledge are built into the framework in a modular fa...
Background: Significance analysis at single gene level may suffer from the limited number of samples and experimental noise that can severely limit the power of the chosen statist...
Mirko Francesconi, Daniel Remondini, Nicola Nerett...
Parametric Embedding (PE) has recently been proposed as a general-purpose algorithm for class visualisation. It takes class posteriors produced by a mixture-based clustering algori...
Background: In the Bioinformatics field, a great deal of interest has been given to Non-negative matrix factorization technique (NMF), due to its capability of providing new insig...
Alberto D. Pascual-Montano, Pedro Carmona-Saez, Mo...