7,762 research outputs found
Hierarchical Graphical Models for Multigroup Shape Analysis using Expectation Maximization with Sampling in Kendall's Shape Space
This paper proposes a novel framework for multi-group shape analysis relying
on a hierarchical graphical statistical model on shapes within a population.The
framework represents individual shapes as point setsmodulo translation,
rotation, and scale, following the notion in Kendall shape space.While
individual shapes are derived from their group shape model, each group shape
model is derived from a single population shape model. The hierarchical model
follows the natural organization of population data and the top level in the
hierarchy provides a common frame of reference for multigroup shape analysis,
e.g. classification and hypothesis testing. Unlike typical shape-modeling
approaches, the proposed model is a generative model that defines a joint
distribution of object-boundary data and the shape-model variables.
Furthermore, it naturally enforces optimal correspondences during the process
of model fitting and thereby subsumes the so-called correspondence problem. The
proposed inference scheme employs an expectation maximization (EM) algorithm
that treats the individual and group shape variables as hidden random variables
and integrates them out before estimating the parameters (population mean and
variance and the group variances). The underpinning of the EM algorithm is the
sampling of pointsets, in Kendall shape space, from their posterior
distribution, for which we exploit a highly-efficient scheme based on
Hamiltonian Monte Carlo simulation. Experiments in this paper use the fitted
hierarchical model to perform (1) hypothesis testing for comparison between
pairs of groups using permutation testing and (2) classification for image
retrieval. The paper validates the proposed framework on simulated data and
demonstrates results on real data.Comment: 9 pages, 7 figures, International Conference on Machine Learning 201
Hierarchically-coupled hidden Markov models for learning kinetic rates from single-molecule data
We address the problem of analyzing sets of noisy time-varying signals that
all report on the same process but confound straightforward analyses due to
complex inter-signal heterogeneities and measurement artifacts. In particular
we consider single-molecule experiments which indirectly measure the distinct
steps in a biomolecular process via observations of noisy time-dependent
signals such as a fluorescence intensity or bead position. Straightforward
hidden Markov model (HMM) analyses attempt to characterize such processes in
terms of a set of conformational states, the transitions that can occur between
these states, and the associated rates at which those transitions occur; but
require ad-hoc post-processing steps to combine multiple signals. Here we
develop a hierarchically coupled HMM that allows experimentalists to deal with
inter-signal variability in a principled and automatic way. Our approach is a
generalized expectation maximization hyperparameter point estimation procedure
with variational Bayes at the level of individual time series that learns an
single interpretable representation of the overall data generating process.Comment: 9 pages, 5 figure
DeepNano: Deep Recurrent Neural Networks for Base Calling in MinION Nanopore Reads
Motivation: The MinION device by Oxford Nanopore is the first portable
sequencing device. MinION is able to produce very long reads (reads over
100~kBp were reported), however it suffers from high sequencing error rate. In
this paper, we show that the error rate can be reduced by improving the base
calling process.
Results: We present the first open-source DNA base caller for the MinION
sequencing platform by Oxford Nanopore. By employing carefully crafted
recurrent neural networks, our tool improves the base calling accuracy compared
to the default base caller supplied by the manufacturer. This advance may
further enhance applicability of MinION for genome sequencing and various
clinical applications.
Availability: DeepNano can be downloaded at
http://compbio.fmph.uniba.sk/deepnano/.
Contact: [email protected]
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