15,288 research outputs found
Hierarchical clustering with Membrane Computing.
In this paper we approach the problem of hierarchical clustering through
membrane computing. A specific P system with external output is designed for each
Boolean matrix associated with a finite set of individuals. The computation of the
system allows us to obtain one of the possible classifications in a non-deterministic
way. The amount of resources required in the construction is polynomial in the
number of individuals and of characteristics analyzed.Ministerio de Educación y Ciencia TIN2006-13425Junta de Andalucía TIC-58
Hierarchical Clustering with Membrane Computing
In this paper we approach the problem of hierarchical clustering through membrane computing. A specific P system with external output is designed for each Boolean matrix associated with a finite set of individuals. The computation of the system allows us to obtain one of the possible classifications in a non-deterministic way. The amount of resources required in the construction is polynomial in the number of individuals and of characteristics analyzed
Deep Neural Networks - A Brief History
Introduction to deep neural networks and their history.Comment: 14 pages, 14 figure
A convolutional autoencoder approach for mining features in cellular electron cryo-tomograms and weakly supervised coarse segmentation
Cellular electron cryo-tomography enables the 3D visualization of cellular
organization in the near-native state and at submolecular resolution. However,
the contents of cellular tomograms are often complex, making it difficult to
automatically isolate different in situ cellular components. In this paper, we
propose a convolutional autoencoder-based unsupervised approach to provide a
coarse grouping of 3D small subvolumes extracted from tomograms. We demonstrate
that the autoencoder can be used for efficient and coarse characterization of
features of macromolecular complexes and surfaces, such as membranes. In
addition, the autoencoder can be used to detect non-cellular features related
to sample preparation and data collection, such as carbon edges from the grid
and tomogram boundaries. The autoencoder is also able to detect patterns that
may indicate spatial interactions between cellular components. Furthermore, we
demonstrate that our autoencoder can be used for weakly supervised semantic
segmentation of cellular components, requiring a very small amount of manual
annotation.Comment: Accepted by Journal of Structural Biolog
Analysis of Genetic Interaction Maps Reveals Functional Pleiotropy
Epistatic or genetic interactions, representing the effects of mutations on the phenotypes caused by other mutations, can be very helpful for uncovering functional relationships between genes. Recently, the Epistasis Miniarray Profile (E-MAP) method has emerged as a powerful approach for identifying such interactions systematically. As part of this approach, hierarchical clustering is used to partition genes into groups on the basis of the similarity between their global interaction profiles. Here we present an original biclustering algorithm for identifying groups of functionally related genes from E-MAP data in a manner that allows individual genes to be assigned to more than one functional group. This enables investigation of the pleiotropic nature of gene function, a goal that cannot be achieved with hierarchical clustering. The performance of our algorithm is illustrated by applying it to two E-MAP datasets and an E-MAP-like in silico dataset for the yeast S. cerevisiae. In addition to identifying the majority of the functional modules reported in these studies, our algorithm uncovers many recently documented and novel multi-functional relationships between genes and gene groups
Deep Gaussian Mixture Models
Deep learning is a hierarchical inference method formed by subsequent
multiple layers of learning able to more efficiently describe complex
relationships. In this work, Deep Gaussian Mixture Models are introduced and
discussed. A Deep Gaussian Mixture model (DGMM) is a network of multiple layers
of latent variables, where, at each layer, the variables follow a mixture of
Gaussian distributions. Thus, the deep mixture model consists of a set of
nested mixtures of linear models, which globally provide a nonlinear model able
to describe the data in a very flexible way. In order to avoid
overparameterized solutions, dimension reduction by factor models can be
applied at each layer of the architecture thus resulting in deep mixtures of
factor analysers.Comment: 19 pages, 4 figure
Numerical simulation of electrocardiograms for full cardiac cycles in healthy and pathological conditions
This work is dedicated to the simulation of full cycles of the electrical
activity of the heart and the corresponding body surface potential. The model
is based on a realistic torso and heart anatomy, including ventricles and
atria. One of the specificities of our approach is to model the atria as a
surface, which is the kind of data typically provided by medical imaging for
thin volumes. The bidomain equations are considered in their usual formulation
in the ventricles, and in a surface formulation on the atria. Two ionic models
are used: the Courtemanche-Ramirez-Nattel model on the atria, and the "Minimal
model for human Ventricular action potentials" (MV) by Bueno-Orovio, Cherry and
Fenton in the ventricles. The heart is weakly coupled to the torso by a Robin
boundary condition based on a resistor- capacitor transmission condition.
Various ECGs are simulated in healthy and pathological conditions (left and
right bundle branch blocks, Bachmann's bundle block, Wolff-Parkinson-White
syndrome). To assess the numerical ECGs, we use several qualitative and
quantitative criteria found in the medical literature. Our simulator can also
be used to generate the signals measured by a vest of electrodes. This
capability is illustrated at the end of the article
- …