Mixing and non-mixing local minima of the entropy contrast for blind source separation

In this paper, both non-mixing and mixing local minima of the entropy are analyzed from the viewpoint of blind source separation (BSS); they correspond respectively to acceptable and spurious solutions of the BSS problem. The contribution of this work is twofold. First, a Taylor development is used to show that the \textit{exact} output entropy cost function has a non-mixing minimum when this output is proportional to \textit{any} of the non-Gaussian sources, and not only when the output is proportional to the lowest entropic source. Second, in order to prove that mixing entropy minima exist when the source densities are strongly multimodal, an entropy approximator is proposed. The latter has the major advantage that an error bound can be provided. Even if this approximator (and the associated bound) is used here in the BSS context, it can be applied for estimating the entropy of any random variable with multimodal density.Comment: 11 pages, 6 figures, To appear in IEEE Transactions on Information Theor

Perturbation Detection Through Modeling of Gene Expression on a Latent Biological Pathway Network: A Bayesian hierarchical approach

Cellular response to a perturbation is the result of a dynamic system of biological variables linked in a complex network. A major challenge in drug and disease studies is identifying the key factors of a biological network that are essential in determining the cell's fate. Here our goal is the identification of perturbed pathways from high-throughput gene expression data. We develop a three-level hierarchical model, where (i) the first level captures the relationship between gene expression and biological pathways using confirmatory factor analysis, (ii) the second level models the behavior within an underlying network of pathways induced by an unknown perturbation using a conditional autoregressive model, and (iii) the third level is a spike-and-slab prior on the perturbations. We then identify perturbations through posterior-based variable selection. We illustrate our approach using gene transcription drug perturbation profiles from the DREAM7 drug sensitivity predication challenge data set. Our proposed method identified regulatory pathways that are known to play a causative role and that were not readily resolved using gene set enrichment analysis or exploratory factor models. Simulation results are presented assessing the performance of this model relative to a network-free variant and its robustness to inaccuracies in biological databases

Low-Energy Properties of a One-dimensional System of Interacting bosons with Boundaries

The ground state properties and low-lying excitations of a (quasi) one-dimensional system of longitudinally confined interacting bosons are studied. This is achieved by extending Haldane's harmonic-fluid description to open boundary conditions. The boson density, one-particle density matrix, and momentum distribution are obtained accounting for finite-size and boundary effects. Friedel oscillations are found in the density. Finite-size scaling of the momentum distribution at zero momentum is proposed as a method to obtain from the experiment the exponent that governs phase correlations. The strong correlations between bosons induced by reduced dimensionality and interactions are displayed by a Bijl-Jastrow wave function for the ground state, which is also derived.Comment: Final published version. Minor changes with respect to the previous versio

Distinct subpopulations of enteric neuronal progenitors defined by time of development, sympathoadrenal lineage markers and Mash-1-dependence

Enteric and sympathetic neurons have previously been proposed to be lineally related. We present independent lines of evidence that suggest that enteric neurons arise from at least two lineages, only one of which expresses markers in common with sympathoadrenal cells. In the rat, sympathoadrenal markers are expressed, in the same order as in sympathetic neurons, by a subset of enteric neuronal precursors, which also transiently express tyrosine hydroxylase. If this precursor pool is eliminated in vitro by complement-mediated lysis, enteric neurons continue to develop; however, none of these are serotonergic. In the mouse, the Mash-1−/− mutation, which eliminates sympathetic neurons, also prevents the development of enteric serotonergic neurons. Other enteric neuronal populations, however, including those that contain calcitonin gene related peptide are present. Enteric tyrosine hydroxylase-containing cells co-express Mash-1 and are eliminated by the Mash-1−/− mutation, consistent with the idea that in the mouse, as in the rat, these precursors generate serotonergic neurons. Serotonergic neurons are generated early in development, while calcitonin gene related peptide-containing enteric neurons are generated much later. These data suggest that enteric neurons are derived from at least two progenitor lineages. One transiently expresses sympathoadrenal markers, is Mash-1-dependent, and generates early-born enteric neurons, some of which are serotonergic. The other is Mash-1-independent, does not express sympathoadrenal markers, and generates late-born enteric neurons, some of which contain calcitonin gene related peptide

Improvement of experimental testing and network training conditions with genome-wide microarrays for more accurate predictions of drug gene targets

BACKGROUND: Genome-wide microarrays have been useful for predicting chemical-genetic interactions at the gene level. However, interpreting genome-wide microarray results can be overwhelming due to the vast output of gene expression data combined with off-target transcriptional responses many times induced by a drug treatment. This study demonstrates how experimental and computational methods can interact with each other, to arrive at more accurate predictions of drug-induced perturbations. We present a two-stage strategy that links microarray experimental testing and network training conditions to predict gene perturbations for a drug with a known mechanism of action in a well-studied organism. RESULTS: S. cerevisiae cells were treated with the antifungal, fluconazole, and expression profiling was conducted under different biological conditions using Affymetrix genome-wide microarrays. Transcripts were filtered with a formal network-based method, sparse simultaneous equation models and Lasso regression (SSEM-Lasso), under different network training conditions. Gene expression results were evaluated using both gene set and single gene target analyses, and the drug’s transcriptional effects were narrowed first by pathway and then by individual genes. Variables included: (i) Testing conditions – exposure time and concentration and (ii) Network training conditions – training compendium modifications. Two analyses of SSEM-Lasso output – gene set and single gene – were conducted to gain a better understanding of how SSEM-Lasso predicts perturbation targets. CONCLUSIONS: This study demonstrates that genome-wide microarrays can be optimized using a two-stage strategy for a more in-depth understanding of how a cell manifests biological reactions to a drug treatment at the transcription level. Additionally, a more detailed understanding of how the statistical model, SSEM-Lasso, propagates perturbations through a network of gene regulatory interactions is achieved.Published versio

Self-organizing Structured RDF in MonetDB

The semantic web uses RDF as its data model, providing ultimate flexibility for users to represent and evolve data without need of a schema. Yet, this flexibility poses challenges in implementing efficient RDF stores, leading from plans with very many self-joins to a triple table, difficulties to optimize these, and a lack of data locality since without a notion of multi-attribute data structure, clustered indexing opportunities are lost. Apart from performance issues, users of huge RDF graphs often have problems formulating queries as they lack any system-supported notion of the structure in the data. In this research, we exploit the observation that real RDF data, while not as regularly structured as relational data, still has the great majority of triples conforming to regular patterns. We conjecture that a system that would recognize this structure automatically would both allow RDF stores to become more efficient and also easier to use. Concretely, we propose to derive self-organizing RDF that stores data in PSO format in such a way that the regular parts of the data physically correspond to relational columnar storage; and propose RDFscan/RDFjoin algorithms that compute star-patterns over these without wasting effort in self-joins. These regular parts, i.e. tables, are identified on ingestion by a schema discovery algorithm -- as such users will gain an SQL view of the regular part of the RDF data. This research aims to produce a state-of-the-art SPARQL frontend for MonetDB as a by-product, and we already present some preliminary results on this platform

Neural Relax

We present an algorithm for data preprocessing of an associative memory inspired to an electrostatic problem that turns out to have intimate relations with information maximization