17,711 research outputs found
Prediction of protein-protein interactions using one-class classification methods and integrating diverse data
This research addresses the problem of prediction of protein-protein interactions (PPI)
when integrating diverse kinds of biological information. This task has been commonly
viewed as a binary classification problem (whether any two proteins do or do not interact)
and several different machine learning techniques have been employed to solve this
task. However the nature of the data creates two major problems which can affect results.
These are firstly imbalanced class problems due to the number of positive examples (pairs
of proteins which really interact) being much smaller than the number of negative ones.
Secondly the selection of negative examples can be based on some unreliable assumptions
which could introduce some bias in the classification results.
Here we propose the use of one-class classification (OCC) methods to deal with the task of
prediction of PPI. OCC methods utilise examples of just one class to generate a predictive
model which consequently is independent of the kind of negative examples selected; additionally
these approaches are known to cope with imbalanced class problems. We have
designed and carried out a performance evaluation study of several OCC methods for this
task, and have found that the Parzen density estimation approach outperforms the rest. We
also undertook a comparative performance evaluation between the Parzen OCC method
and several conventional learning techniques, considering different scenarios, for example
varying the number of negative examples used for training purposes. We found that the
Parzen OCC method in general performs competitively with traditional approaches and in
many situations outperforms them. Finally we evaluated the ability of the Parzen OCC
approach to predict new potential PPI targets, and validated these results by searching for
biological evidence in the literature
Thermodynamic quantum critical behavior of the Kondo necklace model
We obtain the phase diagram and thermodynamic behavior of the Kondo necklace
model for arbitrary dimensions using a representation for the localized and
conduction electrons in terms of local Kondo singlet and triplet operators. A
decoupling scheme on the double time Green's functions yields the dispersion
relation for the excitations of the system. We show that in there is
an antiferromagnetically ordered state at finite temperatures terminating at a
quantum critical point (QCP). In 2-d, long range magnetic order occurs only at
T=0. The line of Neel transitions for varies with the distance to the
quantum critical point QCP as, where the shift
exponent . In the paramagnetic side of the phase diagram, the
spin gap behaves as for consistent with
the value found for the dynamical critical exponent. We also find in this
region a power law temperature dependence in the specific heat for
and along the non-Fermi liquid trajectory. For , in the so-called Kondo spin liquid phase, the thermodynamic
behavior is dominated by an exponential temperature dependence.Comment: Submitted to PR
Synchronization of Excitatory Neurons with Strongly Heterogeneous Phase Responses
In many real-world oscillator systems, the phase response curves are highly
heterogeneous. However, dynamics of heterogeneous oscillator networks has not
been seriously addressed. We propose a theoretical framework to analyze such a
system by dealing explicitly with the heterogeneous phase response curves. We
develop a novel method to solve the self-consistent equations for order
parameters by using formal complex-valued phase variables, and apply our theory
to networks of in vitro cortical neurons. We find a novel state transition that
is not observed in previous oscillator network models.Comment: 4 pages, 3 figure
Experimentally Attainable Optimal Pulse Shapes Obtained with the Aid of Genetic Algorithms
We propose a methodology to design optimal pulses for achieving quantum
optimal control on molecular systems. Our approach constrains pulse shapes to
linear combinations of a fixed number of experimentally relevant pulse
functions. Quantum optimal control is obtained by maximizing a multi-target
fitness function with genetic algorithms. As a first application of the
methodology we generated an optimal pulse that successfully maximized the yield
on a selected dissociation channel of a diatomic molecule. Our pulse is
obtained as a linear combination of linearly chirped pulse functions. Data
recorded along the evolution of the genetic algorithm contained important
information regarding the interplay between radiative and diabatic processes.
We performed a principal component analysis on these data to retrieve the most
relevant processes along the optimal path. Our proposed methodology could be
useful for performing quantum optimal control on more complex systems by
employing a wider variety of pulse shape functions.Comment: 7 pages, 6 figure
Bose-Einstein condensation in antiferromagnets close to the saturation field
At zero temperature and strong applied magnetic fields the ground sate of an
anisotropic antiferromagnet is a saturated paramagnet with fully aligned spins.
We study the quantum phase transition as the field is reduced below an upper
critical and the system enters a XY-antiferromagnetic phase. Using a
bond operator representation we consider a model spin-1 Heisenberg
antiferromagnetic with single-ion anisotropy in hyper-cubic lattices under
strong magnetic fields. We show that the transition at can be
interpreted as a Bose-Einstein condensation (BEC) of magnons. The theoretical
results are used to analyze our magnetization versus field data in the organic
compound - (DTN) at very low temperatures. This is the
ideal BEC system to study this transition since is sufficiently low to
be reached with static magnetic fields (as opposed to pulsed fields). The
scaling of the magnetization as a function of field and temperature close to
shows excellent agreement with the theoretical predictions. It allows
to obtain the quantum critical exponents and confirm the BEC nature of the
transition at .Comment: 4 pages, 1 figure. Accepted for publication in PRB
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