39,201 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
Socially Constrained Structural Learning for Groups Detection in Crowd
Modern crowd theories agree that collective behavior is the result of the
underlying interactions among small groups of individuals. In this work, we
propose a novel algorithm for detecting social groups in crowds by means of a
Correlation Clustering procedure on people trajectories. The affinity between
crowd members is learned through an online formulation of the Structural SVM
framework and a set of specifically designed features characterizing both their
physical and social identity, inspired by Proxemic theory, Granger causality,
DTW and Heat-maps. To adhere to sociological observations, we introduce a loss
function (G-MITRE) able to deal with the complexity of evaluating group
detection performances. We show our algorithm achieves state-of-the-art results
when relying on both ground truth trajectories and tracklets previously
extracted by available detector/tracker systems
Sequential anomaly detection in the presence of noise and limited feedback
This paper describes a methodology for detecting anomalies from sequentially
observed and potentially noisy data. The proposed approach consists of two main
elements: (1) {\em filtering}, or assigning a belief or likelihood to each
successive measurement based upon our ability to predict it from previous noisy
observations, and (2) {\em hedging}, or flagging potential anomalies by
comparing the current belief against a time-varying and data-adaptive
threshold. The threshold is adjusted based on the available feedback from an
end user. Our algorithms, which combine universal prediction with recent work
on online convex programming, do not require computing posterior distributions
given all current observations and involve simple primal-dual parameter
updates. At the heart of the proposed approach lie exponential-family models
which can be used in a wide variety of contexts and applications, and which
yield methods that achieve sublinear per-round regret against both static and
slowly varying product distributions with marginals drawn from the same
exponential family. Moreover, the regret against static distributions coincides
with the minimax value of the corresponding online strongly convex game. We
also prove bounds on the number of mistakes made during the hedging step
relative to the best offline choice of the threshold with access to all
estimated beliefs and feedback signals. We validate the theory on synthetic
data drawn from a time-varying distribution over binary vectors of high
dimensionality, as well as on the Enron email dataset.Comment: 19 pages, 12 pdf figures; final version to be published in IEEE
Transactions on Information Theor
Evidence accumulation in a Laplace domain decision space
Evidence accumulation models of simple decision-making have long assumed that
the brain estimates a scalar decision variable corresponding to the
log-likelihood ratio of the two alternatives. Typical neural implementations of
this algorithmic cognitive model assume that large numbers of neurons are each
noisy exemplars of the scalar decision variable. Here we propose a neural
implementation of the diffusion model in which many neurons construct and
maintain the Laplace transform of the distance to each of the decision bounds.
As in classic findings from brain regions including LIP, the firing rate of
neurons coding for the Laplace transform of net accumulated evidence grows to a
bound during random dot motion tasks. However, rather than noisy exemplars of a
single mean value, this approach makes the novel prediction that firing rates
grow to the bound exponentially, across neurons there should be a distribution
of different rates. A second set of neurons records an approximate inversion of
the Laplace transform, these neurons directly estimate net accumulated
evidence. In analogy to time cells and place cells observed in the hippocampus
and other brain regions, the neurons in this second set have receptive fields
along a "decision axis." This finding is consistent with recent findings from
rodent recordings. This theoretical approach places simple evidence
accumulation models in the same mathematical language as recent proposals for
representing time and space in cognitive models for memory.Comment: Revised for CB
Low-frequency oscillatory correlates of auditory predictive processing in cortical-subcortical networks: a MEG-study
Emerging evidence supports the role of neural oscillations as a mechanism for predictive information processing across large-scale networks. However, the oscillatory signatures underlying auditory mismatch detection and information flow between brain regions remain unclear. To address this issue, we examined the contribution of oscillatory activity at theta/alpha-bands (4–8/8–13 Hz) and assessed directed connectivity in magnetoencephalographic data while 17 human participants were presented with sound sequences containing predictable repetitions and order manipulations that elicited prediction-error responses. We characterized the spectro-temporal properties of neural generators using a minimum-norm approach and assessed directed connectivity using Granger Causality analysis. Mismatching sequences elicited increased theta power and phase-locking in auditory, hippocampal and prefrontal cortices, suggesting that theta-band oscillations underlie prediction-error generation in cortical-subcortical networks. Furthermore, enhanced feedforward theta/alpha-band connectivity was observed in auditory-prefrontal networks during mismatching sequences, while increased feedback connectivity in the alpha-band was observed between hippocampus and auditory regions during predictable sounds. Our findings highlight the involvement of hippocampal theta/alpha-band oscillations towards auditory prediction-error generation and suggest a spectral dissociation between inter-areal feedforward vs. feedback signalling, thus providing novel insights into the oscillatory mechanisms underlying auditory predictive processing
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