20,247 research outputs found
The EM Algorithm and the Rise of Computational Biology
In the past decade computational biology has grown from a cottage industry
with a handful of researchers to an attractive interdisciplinary field,
catching the attention and imagination of many quantitatively-minded
scientists. Of interest to us is the key role played by the EM algorithm during
this transformation. We survey the use of the EM algorithm in a few important
computational biology problems surrounding the "central dogma"; of molecular
biology: from DNA to RNA and then to proteins. Topics of this article include
sequence motif discovery, protein sequence alignment, population genetics,
evolutionary models and mRNA expression microarray data analysis.Comment: Published in at http://dx.doi.org/10.1214/09-STS312 the Statistical
Science (http://www.imstat.org/sts/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Generalized Species Sampling Priors with Latent Beta reinforcements
Many popular Bayesian nonparametric priors can be characterized in terms of
exchangeable species sampling sequences. However, in some applications,
exchangeability may not be appropriate. We introduce a {novel and
probabilistically coherent family of non-exchangeable species sampling
sequences characterized by a tractable predictive probability function with
weights driven by a sequence of independent Beta random variables. We compare
their theoretical clustering properties with those of the Dirichlet Process and
the two parameters Poisson-Dirichlet process. The proposed construction
provides a complete characterization of the joint process, differently from
existing work. We then propose the use of such process as prior distribution in
a hierarchical Bayes modeling framework, and we describe a Markov Chain Monte
Carlo sampler for posterior inference. We evaluate the performance of the prior
and the robustness of the resulting inference in a simulation study, providing
a comparison with popular Dirichlet Processes mixtures and Hidden Markov
Models. Finally, we develop an application to the detection of chromosomal
aberrations in breast cancer by leveraging array CGH data.Comment: For correspondence purposes, Edoardo M. Airoldi's email is
[email protected]; Federico Bassetti's email is
[email protected]; Michele Guindani's email is
[email protected] ; Fabrizo Leisen's email is
[email protected]. To appear in the Journal of the American
Statistical Associatio
Modeling Individual Cyclic Variation in Human Behavior
Cycles are fundamental to human health and behavior. However, modeling cycles
in time series data is challenging because in most cases the cycles are not
labeled or directly observed and need to be inferred from multidimensional
measurements taken over time. Here, we present CyHMMs, a cyclic hidden Markov
model method for detecting and modeling cycles in a collection of
multidimensional heterogeneous time series data. In contrast to previous cycle
modeling methods, CyHMMs deal with a number of challenges encountered in
modeling real-world cycles: they can model multivariate data with discrete and
continuous dimensions; they explicitly model and are robust to missing data;
and they can share information across individuals to model variation both
within and between individual time series. Experiments on synthetic and
real-world health-tracking data demonstrate that CyHMMs infer cycle lengths
more accurately than existing methods, with 58% lower error on simulated data
and 63% lower error on real-world data compared to the best-performing
baseline. CyHMMs can also perform functions which baselines cannot: they can
model the progression of individual features/symptoms over the course of the
cycle, identify the most variable features, and cluster individual time series
into groups with distinct characteristics. Applying CyHMMs to two real-world
health-tracking datasets -- of menstrual cycle symptoms and physical activity
tracking data -- yields important insights including which symptoms to expect
at each point during the cycle. We also find that people fall into several
groups with distinct cycle patterns, and that these groups differ along
dimensions not provided to the model. For example, by modeling missing data in
the menstrual cycles dataset, we are able to discover a medically relevant
group of birth control users even though information on birth control is not
given to the model.Comment: Accepted at WWW 201
Multiple Testing for Neuroimaging via Hidden Markov Random Field
Traditional voxel-level multiple testing procedures in neuroimaging, mostly
-value based, often ignore the spatial correlations among neighboring voxels
and thus suffer from substantial loss of power. We extend the
local-significance-index based procedure originally developed for the hidden
Markov chain models, which aims to minimize the false nondiscovery rate subject
to a constraint on the false discovery rate, to three-dimensional neuroimaging
data using a hidden Markov random field model. A generalized
expectation-maximization algorithm for maximizing the penalized likelihood is
proposed for estimating the model parameters. Extensive simulations show that
the proposed approach is more powerful than conventional false discovery rate
procedures. We apply the method to the comparison between mild cognitive
impairment, a disease status with increased risk of developing Alzheimer's or
another dementia, and normal controls in the FDG-PET imaging study of the
Alzheimer's Disease Neuroimaging Initiative.Comment: A MATLAB package implementing the proposed FDR procedure is available
with this paper at the Biometrics website on Wiley Online Librar
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