1,087 research outputs found
Augmented Sparse Reconstruction of Protein Signaling Networks
The problem of reconstructing and identifying intracellular protein signaling
and biochemical networks is of critical importance in biology today. We sought
to develop a mathematical approach to this problem using, as a test case, one
of the most well-studied and clinically important signaling networks in biology
today, the epidermal growth factor receptor (EGFR) driven signaling cascade.
More specifically, we suggest a method, augmented sparse reconstruction, for
the identification of links among nodes of ordinary differential equation (ODE)
networks from a small set of trajectories with different initial conditions.
Our method builds a system of representation by using a collection of integrals
of all given trajectories and by attenuating block of terms in the
representation itself. The system of representation is then augmented with
random vectors, and minimization of the 1-norm is used to find sparse
representations for the dynamical interactions of each node. Augmentation by
random vectors is crucial, since sparsity alone is not able to handle the large
error-in-variables in the representation. Augmented sparse reconstruction
allows to consider potentially very large spaces of models and it is able to
detect with high accuracy the few relevant links among nodes, even when
moderate noise is added to the measured trajectories. After showing the
performance of our method on a model of the EGFR protein network, we sketch
briefly the potential future therapeutic applications of this approach.Comment: 24 pages, 6 figure
A convex formulation for hyperspectral image superresolution via subspace-based regularization
Hyperspectral remote sensing images (HSIs) usually have high spectral
resolution and low spatial resolution. Conversely, multispectral images (MSIs)
usually have low spectral and high spatial resolutions. The problem of
inferring images which combine the high spectral and high spatial resolutions
of HSIs and MSIs, respectively, is a data fusion problem that has been the
focus of recent active research due to the increasing availability of HSIs and
MSIs retrieved from the same geographical area.
We formulate this problem as the minimization of a convex objective function
containing two quadratic data-fitting terms and an edge-preserving regularizer.
The data-fitting terms account for blur, different resolutions, and additive
noise. The regularizer, a form of vector Total Variation, promotes
piecewise-smooth solutions with discontinuities aligned across the
hyperspectral bands.
The downsampling operator accounting for the different spatial resolutions,
the non-quadratic and non-smooth nature of the regularizer, and the very large
size of the HSI to be estimated lead to a hard optimization problem. We deal
with these difficulties by exploiting the fact that HSIs generally "live" in a
low-dimensional subspace and by tailoring the Split Augmented Lagrangian
Shrinkage Algorithm (SALSA), which is an instance of the Alternating Direction
Method of Multipliers (ADMM), to this optimization problem, by means of a
convenient variable splitting. The spatial blur and the spectral linear
operators linked, respectively, with the HSI and MSI acquisition processes are
also estimated, and we obtain an effective algorithm that outperforms the
state-of-the-art, as illustrated in a series of experiments with simulated and
real-life data.Comment: IEEE Trans. Geosci. Remote Sens., to be publishe
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