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High-dimensional data sets generated by high-throughput technologies, such as DNA microarray, are often the outputs of complex networked systems driven by hidden regulatory signals. Traditional statistical methods for computing low-dimensional or hidden representations of these data sets, such as principal component analysis and independent component(More)
In this paper, we introduce a novel independent component analysis (ICA) algorithm, which is truly blind to the particular underlying distribution of the mixed signals. Using a nonparametric kernel density estimation technique, the algorithm performs simultaneously the estimation of the unknown probability density functions of the source signals and the(More)
The authors recently introduced a framework, named Network Component Analysis (NCA), for the reconstruction of the dynamics of transcriptional regulators' activities from gene expression assays. The original formulation had certain shortcomings that limited NCA's application to a wide class of network dynamics reconstruction problems, either because of(More)
We introduce a novel approach to the blind signal separation (BSS) problem that is capable of jointly estimating the probability density function (pdf) of the source signals and the unmixing matrix. We demonstrate that, using a kernel density estimation based Projection Pursuit (PP) algorithm , it is possible to extract, from instantaneous mixtures ,(More)
In this article, we introduce an exploratory framework for learning patterns of conditional co-expression in gene expression data. The main idea behind the proposed approach consists of estimating how the information content shared by a set of M nodes in a network (where each node is associated to an expression profile) varies upon conditioning on a set of(More)
The promise of discovering a functional blueprint of a cellular system from large-scale and high-throughput sequence and experimental data is predicated on the belief that the same top-down investigative approach that proved successful in other biological problems (e.g. DNA sequencing) will be as effective when it comes to inferring more complex(More)
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