644 research outputs found
In All Likelihood, Deep Belief Is Not Enough
Statistical models of natural stimuli provide an important tool for
researchers in the fields of machine learning and computational neuroscience. A
canonical way to quantitatively assess and compare the performance of
statistical models is given by the likelihood. One class of statistical models
which has recently gained increasing popularity and has been applied to a
variety of complex data are deep belief networks. Analyses of these models,
however, have been typically limited to qualitative analyses based on samples
due to the computationally intractable nature of the model likelihood.
Motivated by these circumstances, the present article provides a consistent
estimator for the likelihood that is both computationally tractable and simple
to apply in practice. Using this estimator, a deep belief network which has
been suggested for the modeling of natural image patches is quantitatively
investigated and compared to other models of natural image patches. Contrary to
earlier claims based on qualitative results, the results presented in this
article provide evidence that the model under investigation is not a
particularly good model for natural image
Data-driven modelling of biological multi-scale processes
Biological processes involve a variety of spatial and temporal scales. A
holistic understanding of many biological processes therefore requires
multi-scale models which capture the relevant properties on all these scales.
In this manuscript we review mathematical modelling approaches used to describe
the individual spatial scales and how they are integrated into holistic models.
We discuss the relation between spatial and temporal scales and the implication
of that on multi-scale modelling. Based upon this overview over
state-of-the-art modelling approaches, we formulate key challenges in
mathematical and computational modelling of biological multi-scale and
multi-physics processes. In particular, we considered the availability of
analysis tools for multi-scale models and model-based multi-scale data
integration. We provide a compact review of methods for model-based data
integration and model-based hypothesis testing. Furthermore, novel approaches
and recent trends are discussed, including computation time reduction using
reduced order and surrogate models, which contribute to the solution of
inference problems. We conclude the manuscript by providing a few ideas for the
development of tailored multi-scale inference methods.Comment: This manuscript will appear in the Journal of Coupled Systems and
Multiscale Dynamics (American Scientific Publishers
The Heckscher-Ohlin Model and the Network Structure of International Trade
This paper estimates for 28 product groups a characteristic parameter that reflects the topological structure of its trading network. Using these estimates, it then describes how the structure of international trade has evolved during the 1980-2000 period. Thereafter, it demonstrates the importance of networks in international trade by explicitly accounting for their scaling properties when testing the prediction of the Heckscher-Ohlin model that factor endowment differentials determine bilateral trade flows. The results suggest that differences in factor endowments increase bilateral trade in goods that are traded in "dispersed" networks. For goods that are traded in "concentrated" networks, factor endowment differentials are less important.Networks, international trade, gravity model
Which Sectors of a Modern Economy are most Central?
We analyze input-output matrices for a wide set of countries as weighted directed networks. These graphs contain only 47 nodes, but they are almost fully connected and many have nodes with strong self-loops. We apply two measures: random walk centrality and one based on count-betweenness. Our findings are intuitive. For example, in Luxembourg the most central sector is “Finance and Insurance” and the analog in Germany is “Wholesale and Retail Trade” or “Motor Vehicles”, according to the measure. Rankings of sectoral centrality vary by country. Some sectors are often highly central, while others never are. Hierarchical clustering reveals geographical proximity and similar development status.
MCA: Multiresolution Correlation Analysis, a graphical tool for subpopulation identification in single-cell gene expression data
Background: Biological data often originate from samples containing mixtures
of subpopulations, corresponding e.g. to distinct cellular phenotypes. However,
identification of distinct subpopulations may be difficult if biological
measurements yield distributions that are not easily separable. Results: We
present Multiresolution Correlation Analysis (MCA), a method for visually
identifying subpopulations based on the local pairwise correlation between
covariates, without needing to define an a priori interaction scale. We
demonstrate that MCA facilitates the identification of differentially regulated
subpopulations in simulated data from a small gene regulatory network, followed
by application to previously published single-cell qPCR data from mouse
embryonic stem cells. We show that MCA recovers previously identified
subpopulations, provides additional insight into the underlying correlation
structure, reveals potentially spurious compartmentalizations, and provides
insight into novel subpopulations. Conclusions: MCA is a useful method for the
identification of subpopulations in low-dimensional expression data, as
emerging from qPCR or FACS measurements. With MCA it is possible to investigate
the robustness of covariate correlations with respect subpopulations,
graphically identify outliers, and identify factors contributing to
differential regulation between pairs of covariates. MCA thus provides a
framework for investigation of expression correlations for genes of interests
and biological hypothesis generation.Comment: BioVis 2014 conferenc
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MPRAnalyze: statistical framework for massively parallel reporter assays.
Massively parallel reporter assays (MPRAs) can measure the regulatory function of thousands of DNA sequences in a single experiment. Despite growing popularity, MPRA studies are limited by a lack of a unified framework for analyzing the resulting data. Here we present MPRAnalyze: a statistical framework for analyzing MPRA count data. Our model leverages the unique structure of MPRA data to quantify the function of regulatory sequences, compare sequences' activity across different conditions, and provide necessary flexibility in an evolving field. We demonstrate the accuracy and applicability of MPRAnalyze on simulated and published data and compare it with existing methods
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