37,522 research outputs found
Toward a multilevel representation of protein molecules: comparative approaches to the aggregation/folding propensity problem
This paper builds upon the fundamental work of Niwa et al. [34], which
provides the unique possibility to analyze the relative aggregation/folding
propensity of the elements of the entire Escherichia coli (E. coli) proteome in
a cell-free standardized microenvironment. The hardness of the problem comes
from the superposition between the driving forces of intra- and inter-molecule
interactions and it is mirrored by the evidences of shift from folding to
aggregation phenotypes by single-point mutations [10]. Here we apply several
state-of-the-art classification methods coming from the field of structural
pattern recognition, with the aim to compare different representations of the
same proteins gathered from the Niwa et al. data base; such representations
include sequences and labeled (contact) graphs enriched with chemico-physical
attributes. By this comparison, we are able to identify also some interesting
general properties of proteins. Notably, (i) we suggest a threshold around 250
residues discriminating "easily foldable" from "hardly foldable" molecules
consistent with other independent experiments, and (ii) we highlight the
relevance of contact graph spectra for folding behavior discrimination and
characterization of the E. coli solubility data. The soundness of the
experimental results presented in this paper is proved by the statistically
relevant relationships discovered among the chemico-physical description of
proteins and the developed cost matrix of substitution used in the various
discrimination systems.Comment: 17 pages, 3 figures, 46 reference
On the Intrinsic Locality Properties of Web Reference Streams
There has been considerable work done in the study of Web reference streams: sequences of requests for Web objects. In particular, many studies have looked at the locality properties of such streams, because of the impact of locality on the design and performance of caching and prefetching systems. However, a general framework for understanding why reference streams exhibit given locality properties has not yet emerged.
In this work we take a first step in this direction, based on viewing the Web as a set of reference streams that are transformed by Web components (clients, servers, and intermediaries). We propose a graph-based framework for describing this collection of streams and components. We identify three basic stream transformations that occur at nodes of the graph: aggregation, disaggregation and filtering, and we show how these transformations can be used to abstract the effects of different Web components on their associated reference streams. This view allows a structured approach to the analysis of why reference streams show given properties at different points in the Web.
Applying this approach to the study of locality requires good metrics for locality. These metrics must meet three criteria: 1) they must accurately capture temporal locality; 2) they must be independent of trace artifacts such as trace length; and 3) they must not involve manual procedures or model-based assumptions. We describe two metrics meeting these criteria that each capture a different kind of temporal locality in reference streams. The popularity component of temporal locality is captured by entropy, while the correlation component is captured by interreference coefficient of variation. We argue that these metrics are more natural and more useful than previously proposed metrics for temporal locality.
We use this framework to analyze a diverse set of Web reference traces. We find that this framework can shed light on how and why locality properties vary across different locations in the Web topology. For example, we find that filtering and aggregation have opposing effects on the popularity component of the temporal locality, which helps to explain why multilevel caching can be effective in the Web. Furthermore, we find that all transformations tend to diminish the correlation component of temporal locality, which has implications for the utility of different cache replacement policies at different points in the Web.National Science Foundation (ANI-9986397, ANI-0095988); CNPq-Brazi
Analysis of heat kernel highlights the strongly modular and heat-preserving structure of proteins
In this paper, we study the structure and dynamical properties of protein
contact networks with respect to other biological networks, together with
simulated archetypal models acting as probes. We consider both classical
topological descriptors, such as the modularity and statistics of the shortest
paths, and different interpretations in terms of diffusion provided by the
discrete heat kernel, which is elaborated from the normalized graph Laplacians.
A principal component analysis shows high discrimination among the network
types, either by considering the topological and heat kernel based vector
characterizations. Furthermore, a canonical correlation analysis demonstrates
the strong agreement among those two characterizations, providing thus an
important justification in terms of interpretability for the heat kernel.
Finally, and most importantly, the focused analysis of the heat kernel provides
a way to yield insights on the fact that proteins have to satisfy specific
structural design constraints that the other considered networks do not need to
obey. Notably, the heat trace decay of an ensemble of varying-size proteins
denotes subdiffusion, a peculiar property of proteins
Proper local scoring rules on discrete sample spaces
A scoring rule is a loss function measuring the quality of a quoted
probability distribution for a random variable , in the light of the
realized outcome of ; it is proper if the expected score, under any
distribution for , is minimized by quoting . Using the fact that
any differentiable proper scoring rule on a finite sample space
is the gradient of a concave homogeneous function, we consider when such a rule
can be local in the sense of depending only on the probabilities quoted for
points in a nominated neighborhood of . Under mild conditions, we
characterize such a proper local scoring rule in terms of a collection of
homogeneous functions on the cliques of an undirected graph on the space
. A useful property of such rules is that the quoted
distribution need only be known up to a scale factor. Examples of the use
of such scoring rules include Besag's pseudo-likelihood and Hyv\"{a}rinen's
method of ratio matching.Comment: Published in at http://dx.doi.org/10.1214/12-AOS972 the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Designing labeled graph classifiers by exploiting the R\'enyi entropy of the dissimilarity representation
Representing patterns as labeled graphs is becoming increasingly common in
the broad field of computational intelligence. Accordingly, a wide repertoire
of pattern recognition tools, such as classifiers and knowledge discovery
procedures, are nowadays available and tested for various datasets of labeled
graphs. However, the design of effective learning procedures operating in the
space of labeled graphs is still a challenging problem, especially from the
computational complexity viewpoint. In this paper, we present a major
improvement of a general-purpose classifier for graphs, which is conceived on
an interplay between dissimilarity representation, clustering,
information-theoretic techniques, and evolutionary optimization algorithms. The
improvement focuses on a specific key subroutine devised to compress the input
data. We prove different theorems which are fundamental to the setting of the
parameters controlling such a compression operation. We demonstrate the
effectiveness of the resulting classifier by benchmarking the developed
variants on well-known datasets of labeled graphs, considering as distinct
performance indicators the classification accuracy, computing time, and
parsimony in terms of structural complexity of the synthesized classification
models. The results show state-of-the-art standards in terms of test set
accuracy and a considerable speed-up for what concerns the computing time.Comment: Revised versio
Characterization of complex networks: A survey of measurements
Each complex network (or class of networks) presents specific topological
features which characterize its connectivity and highly influence the dynamics
of processes executed on the network. The analysis, discrimination, and
synthesis of complex networks therefore rely on the use of measurements capable
of expressing the most relevant topological features. This article presents a
survey of such measurements. It includes general considerations about complex
network characterization, a brief review of the principal models, and the
presentation of the main existing measurements. Important related issues
covered in this work comprise the representation of the evolution of complex
networks in terms of trajectories in several measurement spaces, the analysis
of the correlations between some of the most traditional measurements,
perturbation analysis, as well as the use of multivariate statistics for
feature selection and network classification. Depending on the network and the
analysis task one has in mind, a specific set of features may be chosen. It is
hoped that the present survey will help the proper application and
interpretation of measurements.Comment: A working manuscript with 78 pages, 32 figures. Suggestions of
measurements for inclusion are welcomed by the author
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