8,338 research outputs found
The genome of the medieval Black Death agent (extended abstract)
The genome of a 650 year old Yersinia pestis bacteria, responsible for the
medieval Black Death, was recently sequenced and assembled into 2,105 contigs
from the main chromosome. According to the point mutation record, the medieval
bacteria could be an ancestor of most Yersinia pestis extant species, which
opens the way to reconstructing the organization of these contigs using a
comparative approach. We show that recent computational paleogenomics methods,
aiming at reconstructing the organization of ancestral genomes from the
comparison of extant genomes, can be used to correct, order and complete the
contig set of the Black Death agent genome, providing a full chromosome
sequence, at the nucleotide scale, of this ancient bacteria. This sequence
suggests that a burst of mobile elements insertions predated the Black Death,
leading to an exceptional genome plasticity and increase in rearrangement rate.Comment: Extended abstract of a talk presented at the conference JOBIM 2013,
https://colloque.inra.fr/jobim2013_eng/. Full paper submitte
Reconstructing directed and weighted topologies of phase-locked oscillator networks
The formalism of complex networks is extensively employed to describe the
dynamics of interacting agents in several applications. The features of the
connections among the nodes in a network are not always provided beforehand,
hence the problem of appropriately inferring them often arises. Here, we
present a method to reconstruct directed and weighted topologies (REDRAW) of
networks of heterogeneous phase-locked nonlinear oscillators. We ultimately
plan on using REDRAW to infer the interaction structure in human ensembles
engaged in coordination tasks, and give insights into the overall behavior
Inferring Regulatory Networks by Combining Perturbation Screens and Steady State Gene Expression Profiles
Reconstructing transcriptional regulatory networks is an important task in
functional genomics. Data obtained from experiments that perturb genes by
knockouts or RNA interference contain useful information for addressing this
reconstruction problem. However, such data can be limited in size and/or are
expensive to acquire. On the other hand, observational data of the organism in
steady state (e.g. wild-type) are more readily available, but their
informational content is inadequate for the task at hand. We develop a
computational approach to appropriately utilize both data sources for
estimating a regulatory network. The proposed approach is based on a three-step
algorithm to estimate the underlying directed but cyclic network, that uses as
input both perturbation screens and steady state gene expression data. In the
first step, the algorithm determines causal orderings of the genes that are
consistent with the perturbation data, by combining an exhaustive search method
with a fast heuristic that in turn couples a Monte Carlo technique with a fast
search algorithm. In the second step, for each obtained causal ordering, a
regulatory network is estimated using a penalized likelihood based method,
while in the third step a consensus network is constructed from the highest
scored ones. Extensive computational experiments show that the algorithm
performs well in reconstructing the underlying network and clearly outperforms
competing approaches that rely only on a single data source. Further, it is
established that the algorithm produces a consistent estimate of the regulatory
network.Comment: 24 pages, 4 figures, 6 table
Cutwidth: obstructions and algorithmic aspects
Cutwidth is one of the classic layout parameters for graphs. It measures how
well one can order the vertices of a graph in a linear manner, so that the
maximum number of edges between any prefix and its complement suffix is
minimized. As graphs of cutwidth at most are closed under taking
immersions, the results of Robertson and Seymour imply that there is a finite
list of minimal immersion obstructions for admitting a cut layout of width at
most . We prove that every minimal immersion obstruction for cutwidth at
most has size at most .
As an interesting algorithmic byproduct, we design a new fixed-parameter
algorithm for computing the cutwidth of a graph that runs in time , where is the optimum width and is the number of vertices.
While being slower by a -factor in the exponent than the fastest known
algorithm, given by Thilikos, Bodlaender, and Serna in [Cutwidth I: A linear
time fixed parameter algorithm, J. Algorithms, 56(1):1--24, 2005] and [Cutwidth
II: Algorithms for partial -trees of bounded degree, J. Algorithms,
56(1):25--49, 2005], our algorithm has the advantage of being simpler and
self-contained; arguably, it explains better the combinatorics of optimum-width
layouts
Active Semi-Supervised Learning Using Sampling Theory for Graph Signals
We consider the problem of offline, pool-based active semi-supervised
learning on graphs. This problem is important when the labeled data is scarce
and expensive whereas unlabeled data is easily available. The data points are
represented by the vertices of an undirected graph with the similarity between
them captured by the edge weights. Given a target number of nodes to label, the
goal is to choose those nodes that are most informative and then predict the
unknown labels. We propose a novel framework for this problem based on our
recent results on sampling theory for graph signals. A graph signal is a
real-valued function defined on each node of the graph. A notion of frequency
for such signals can be defined using the spectrum of the graph Laplacian
matrix. The sampling theory for graph signals aims to extend the traditional
Nyquist-Shannon sampling theory by allowing us to identify the class of graph
signals that can be reconstructed from their values on a subset of vertices.
This approach allows us to define a criterion for active learning based on
sampling set selection which aims at maximizing the frequency of the signals
that can be reconstructed from their samples on the set. Experiments show the
effectiveness of our method.Comment: 10 pages, 6 figures, To appear in KDD'1
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