112,520 research outputs found
A decision tree-based method for protein contact map prediction
In this paper, we focus on protein contact map prediction.
We describe a method where contact maps are predicted using decision
tree-based model. The algorithm includes the subsequence information
between the couple of analyzed amino acids. In order to evaluate the
method generalization capabilities, we carry out an experiment using
173 non-homologous proteins of known structures. Our results indicate
that the method can assign protein contacts with an average accuracy of
0.34, superior to the 0.25 obtained by the FNETCSS method. This shows
that our algorithm improves the accuracy with respect to the methods
compared, especially with the increase of protein lengt
Inferring AS Relationships: Dead End or Lively Beginning?
Recent techniques for inferring business relationships between ASs have
yielded maps that have extremely few invalid BGP paths in the terminology of
Gao. However, some relationships inferred by these newer algorithms are
incorrect, leading to the deduction of unrealistic AS hierarchies. We
investigate this problem and discover what causes it. Having obtained such
insight, we generalize the problem of AS relationship inference as a
multiobjective optimization problem with node-degree-based corrections to the
original objective function of minimizing the number of invalid paths. We solve
the generalized version of the problem using the semidefinite programming
relaxation of the MAX2SAT problem. Keeping the number of invalid paths small,
we obtain a more veracious solution than that yielded by recent heuristics
An Algorithm for Preferential Selection of Spectroscopic Targets in LEGUE
We describe a general target selection algorithm that is applicable to any
survey in which the number of available candidates is much larger than the
number of objects to be observed. This routine aims to achieve a balance
between a smoothly-varying, well-understood selection function and the desire
to preferentially select certain types of targets. Some target-selection
examples are shown that illustrate different possibilities of emphasis
functions. Although it is generally applicable, the algorithm was developed
specifically for the LAMOST Experiment for Galactic Understanding and
Exploration (LEGUE) survey that will be carried out using the Chinese Guo Shou
Jing Telescope. In particular, this algorithm was designed for the portion of
LEGUE targeting the Galactic halo, in which we attempt to balance a variety of
science goals that require stars at fainter magnitudes than can be completely
sampled by LAMOST. This algorithm has been implemented for the halo portion of
the LAMOST pilot survey, which began in October 2011.Comment: 17 pages, 7 figures, accepted for publication in RA
Enhancing a Search Algorithm to Perform Intelligent Backtracking
This paper illustrates how a Prolog program, using chronological backtracking
to find a solution in some search space, can be enhanced to perform intelligent
backtracking. The enhancement crucially relies on the impurity of Prolog that
allows a program to store information when a dead end is reached. To illustrate
the technique, a simple search program is enhanced.
To appear in Theory and Practice of Logic Programming.
Keywords: intelligent backtracking, dependency-directed backtracking,
backjumping, conflict-directed backjumping, nogood sets, look-back.Comment: To appear in Theory and Practice of Logic Programmin
Analysis of a Gibbs sampler method for model based clustering of gene expression data
Over the last decade, a large variety of clustering algorithms have been
developed to detect coregulatory relationships among genes from microarray gene
expression data. Model based clustering approaches have emerged as
statistically well grounded methods, but the properties of these algorithms
when applied to large-scale data sets are not always well understood. An
in-depth analysis can reveal important insights about the performance of the
algorithm, the expected quality of the output clusters, and the possibilities
for extracting more relevant information out of a particular data set. We have
extended an existing algorithm for model based clustering of genes to
simultaneously cluster genes and conditions, and used three large compendia of
gene expression data for S. cerevisiae to analyze its properties. The algorithm
uses a Bayesian approach and a Gibbs sampling procedure to iteratively update
the cluster assignment of each gene and condition. For large-scale data sets,
the posterior distribution is strongly peaked on a limited number of
equiprobable clusterings. A GO annotation analysis shows that these local
maxima are all biologically equally significant, and that simultaneously
clustering genes and conditions performs better than only clustering genes and
assuming independent conditions. A collection of distinct equivalent
clusterings can be summarized as a weighted graph on the set of genes, from
which we extract fuzzy, overlapping clusters using a graph spectral method. The
cores of these fuzzy clusters contain tight sets of strongly coexpressed genes,
while the overlaps exhibit relations between genes showing only partial
coexpression.Comment: 8 pages, 7 figure
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