Isabel M. Tienda-Luna

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Statistical models for reverse engineering gene regulatory networks are surveyed in this article. To provide readers with a system-level view of the modeling issues in this research, a graphical modeling framework is proposed. This framework serves as the scaffolding on which the review of different models can be systematically assembled. Based on the(More)
In this paper we study iterative decoding over channels with phase noise in digital communications. A factor graph representation is used for the system under study and a message passing algorithm is derived. In order to reduce the complexity of such algorithm, several methods have been proposed in the literature. One of them consists of a discretization of(More)
The development of new antimalarial drugs is urgently needed due to elevated drug resistance in the causative agents Plasmodium parasites. An intervention strategy based on the interruption of the parasite cell cycle could be undertaken using a systems-biology aided drug discovery approach. However, little is known about the components or the mechanism of(More)
Statistical models for reverse engineering gene regulatory networks are surveyed in this article. To provide readers with a system-level view of the modeling issues in this research, a graphical modeling framework is proposed. This framework serves as the scaffolding on which the review of different models can be systematically assembled. Based on the(More)
We investigate in this paper reverse engineering of gene regulatory networks from time-series microarray data. We apply dynamic Bayesian networks (DBNs) for modeling cell cycle regulations. In developing a network inference algorithm, we focus on soft solutions that can provide a posteriori probability (APP) of network topology. In particular, we propose a(More)
We propose a method for blind multiuser detection (MUD) in synchronous systems over flat and fast Rayleigh fading channels. We adopt an autoregressive-moving-average (ARMA) process to model the temporal correlation of the channels. Based on the ARMA process, we propose a novel time-observation state-space model (TOSSM) that describes the dynamics of the(More)
Uncovering transcription factor (TF) mediated regulatory networks from microarray expression data and prior knowledge is considered in this paper. Bayesian factor models that model direct TF regulation are formulated. To address the enormous computational complexity of the model in large networks, a novel, efficient basis-expansion factor model (BEFaM) has(More)
We have revised the Markov lineal model used in the analysis of microarray time-series data. According to this model, the expression level of a given gene at any specific time is a linear combination of the measured expression levels of other genes at previous time instants, plus noise. The problem of uncovering such relationships can be solved using(More)
In this contribution, the relation between the principal components of the covariance matrix of a hyperspectral image and the spectra of the endmembers is studied. When the data satisfy the spectral mixing model, from this relation the spectra of the endmembers and the abundance of each endmember in the pixels of the image can be theoretically obtained(More)