22,395 research outputs found
Hidden Markov Models for Gene Sequence Classification: Classifying the VSG genes in the Trypanosoma brucei Genome
The article presents an application of Hidden Markov Models (HMMs) for
pattern recognition on genome sequences. We apply HMM for identifying genes
encoding the Variant Surface Glycoprotein (VSG) in the genomes of Trypanosoma
brucei (T. brucei) and other African trypanosomes. These are parasitic protozoa
causative agents of sleeping sickness and several diseases in domestic and wild
animals. These parasites have a peculiar strategy to evade the host's immune
system that consists in periodically changing their predominant cellular
surface protein (VSG). The motivation for using patterns recognition methods to
identify these genes, instead of traditional homology based ones, is that the
levels of sequence identity (amino acid and DNA sequence) amongst these genes
is often below of what is considered reliable in these methods. Among pattern
recognition approaches, HMM are particularly suitable to tackle this problem
because they can handle more naturally the determination of gene edges. We
evaluate the performance of the model using different number of states in the
Markov model, as well as several performance metrics. The model is applied
using public genomic data. Our empirical results show that the VSG genes on T.
brucei can be safely identified (high sensitivity and low rate of false
positives) using HMM.Comment: Accepted article in July, 2015 in Pattern Analysis and Applications,
Springer. The article contains 23 pages, 4 figures, 8 tables and 51
reference
Digital Ecosystems: Ecosystem-Oriented Architectures
We view Digital Ecosystems to be the digital counterparts of biological
ecosystems. Here, we are concerned with the creation of these Digital
Ecosystems, exploiting the self-organising properties of biological ecosystems
to evolve high-level software applications. Therefore, we created the Digital
Ecosystem, a novel optimisation technique inspired by biological ecosystems,
where the optimisation works at two levels: a first optimisation, migration of
agents which are distributed in a decentralised peer-to-peer network, operating
continuously in time; this process feeds a second optimisation based on
evolutionary computing that operates locally on single peers and is aimed at
finding solutions to satisfy locally relevant constraints. The Digital
Ecosystem was then measured experimentally through simulations, with measures
originating from theoretical ecology, evaluating its likeness to biological
ecosystems. This included its responsiveness to requests for applications from
the user base, as a measure of the ecological succession (ecosystem maturity).
Overall, we have advanced the understanding of Digital Ecosystems, creating
Ecosystem-Oriented Architectures where the word ecosystem is more than just a
metaphor.Comment: 39 pages, 26 figures, journa
Dagstuhl Reports : Volume 1, Issue 2, February 2011
Online Privacy: Towards Informational Self-Determination on the Internet (Dagstuhl Perspectives Workshop 11061) : Simone Fischer-Hübner, Chris Hoofnagle, Kai Rannenberg, Michael Waidner, Ioannis Krontiris and Michael Marhöfer Self-Repairing Programs (Dagstuhl Seminar 11062) : Mauro Pezzé, Martin C. Rinard, Westley Weimer and Andreas Zeller Theory and Applications of Graph Searching Problems (Dagstuhl Seminar 11071) : Fedor V. Fomin, Pierre Fraigniaud, Stephan Kreutzer and Dimitrios M. Thilikos Combinatorial and Algorithmic Aspects of Sequence Processing (Dagstuhl Seminar 11081) : Maxime Crochemore, Lila Kari, Mehryar Mohri and Dirk Nowotka Packing and Scheduling Algorithms for Information and Communication Services (Dagstuhl Seminar 11091) Klaus Jansen, Claire Mathieu, Hadas Shachnai and Neal E. Youn
Computational intelligence approaches to robotics, automation, and control [Volume guest editors]
No abstract available
Learning to Prune: Speeding up Repeated Computations
It is common to encounter situations where one must solve a sequence of
similar computational problems. Running a standard algorithm with worst-case
runtime guarantees on each instance will fail to take advantage of valuable
structure shared across the problem instances. For example, when a commuter
drives from work to home, there are typically only a handful of routes that
will ever be the shortest path. A naive algorithm that does not exploit this
common structure may spend most of its time checking roads that will never be
in the shortest path. More generally, we can often ignore large swaths of the
search space that will likely never contain an optimal solution.
We present an algorithm that learns to maximally prune the search space on
repeated computations, thereby reducing runtime while provably outputting the
correct solution each period with high probability. Our algorithm employs a
simple explore-exploit technique resembling those used in online algorithms,
though our setting is quite different. We prove that, with respect to our model
of pruning search spaces, our approach is optimal up to constant factors.
Finally, we illustrate the applicability of our model and algorithm to three
classic problems: shortest-path routing, string search, and linear programming.
We present experiments confirming that our simple algorithm is effective at
significantly reducing the runtime of solving repeated computations
Computational Cancer Biology: An Evolutionary Perspective
ISSN:1553-734XISSN:1553-735
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