Characterization of Caenorhabditis elegans behavior in response to chemical stress by using hidden Markov model

2014 ◽  
Vol 28 (17) ◽  
pp. 1450136 ◽  
Author(s):  
Yeontaek Choi ◽  
Seungwoo Sim ◽  
Sang-Hee Lee
Keyword(s):  
Caenorhabditis Elegans ◽  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Correlation Coefficients ◽  
Branch Length ◽  
Chemical Stress ◽  
Self Organizing Map ◽  
Pattern Sequence ◽  
Locomotion Behavior

The locomotion behavior of Caenorhabditis elegans has been extensively studied to understand the relationship between the changes in the organism's neural activity and the biomechanics. However, so far, we have not yet achieved the understanding. This is because the worm complicatedly responds to the environmental factors, especially chemical stress. Constructing a mathematical model is helpful for the understanding the locomotion behavior in various surrounding conditions. In the present study, we built three hidden Markov models for the crawling behavior of C. elegans in a controlled environment with no chemical treatment and in a polluted environment by formaldehyde, toluene, and benzene (0.1 ppm and 0.5 ppm for each case). The organism's crawling activity was recorded using a digital camcorder for 20 min at a rate of 24 frames per second. All shape patterns were quantified by branch length similarity entropy and classified into five groups by using the self-organizing map. To evaluate and establish the hidden Markov models, we compared correlation coefficients between the simulated behavior (i.e. temporal pattern sequence) generated by the models and the actual crawling behavior. The comparison showed that the hidden Markov models are successful to characterize the crawling behavior. In addition, we briefly discussed the possibility of using the models together with the entropy to develop bio-monitoring systems for determining water quality.

A HYBRID STOCHASTIC-CONNECTIONIST APPROACH TO GESTURE RECOGNITION

2000 ◽  
Vol 09 (02) ◽  
pp. 177-203 ◽  
Author(s):  
ANDREA CORRADINI ◽  
HANS-JOACHIM BOEHME ◽  
HORST-MICHAEL GROSS
Keyword(s):  
Hidden Markov Models ◽  
Gesture Recognition ◽  
Markov Models ◽  
Hidden Markov ◽  
Self Organizing Map ◽  
Localization Task ◽  
Cluttered Environments ◽  
Hybrid Combination ◽  
Dynamic Gestures ◽  
Input Variables

In this paper a person-specific saliency system and subsequently two architectures for the recognition of dynamic gestures are described. The systems implemented are designed to take a sequence of images and to assign it to one of a number of discrete classes where each of them corresponds to a gesture from a predefined small vocabulary. Since we think that for a human-computer interaction the localization of the user is essential for any further step regarding the recognition and the interpretation of gestures, in the first part, we begin with describing our saliency system dedicated to the person localization task in cluttered environments. Successively, the intrinsic gesture recognition process is broken down into an initial preprocessing stage followed by a mapping from the preprocessed input variables to an output variable representing the class label. Subsequently, we utilize two different classifiers for mapping the ordered sequence of feature vectors to one gesture category. The first classifier utilizes a hybrid combination of Kohonen Self-Organizing Map (SOM) and Discrete Hidden Markov Models (DHMM). As second recognizer a system of Continuous Hidden Markov Models (CHMM) is used. Preliminary experiments with our baseline systems are demonstrated.

scholarly journals Inferring Population Size Histories using Coalescent Hidden Markov Models with TMRCA and Total Branch Length as Hidden States

2021 ◽  
Author(s):  
Gautam Upadhya ◽  
Matthias Steinruecken
Keyword(s):  
Population Size ◽  
Hidden Markov Models ◽  
Latent Variable ◽  
Markov Models ◽  
Hidden Markov ◽  
Branch Length ◽  
Quality Data ◽  
Sample Sizes ◽  
Large Sample ◽  
Total Branch Length

Unraveling the complex demographic histories of natural populations is a central problem in population genetics. Understanding past demographic events is of general anthropological interest and is also an important step in establishing accurate null models when identifying adaptive or disease-associated genetic variation. An important class of tools for inferring past population size changes are Coalescent Hidden Markov Models (CHMMs). These models make efficient use of the linkage information in population genomic datasets by using the local genealogies relating sampled individuals as latent states that evolve across the chromosome in an HMM framework. Extending these models to large sample sizes is challenging, since the number of possible latent states increases rapidly. Here, we present our method CHIMP (CHMM History-Inference ML Procedure), a novel CHMM method for inferring the size history of a population. It can be applied to large samples (hundreds of haplotypes) and only requires unphased genomes as input. The two implementations of CHIMP that we present here use either the height of the genealogical tree (TMRCA) or the total branch length, respectively, as the latent variable at each position in the genome. The requisite transition and emission probabilities are obtained by numerically solving certain systems of differential equations derived from the ancestral process with recombination. The parameters of the population size history are subsequently inferred using an Expectation-Maximization algorithm. In addition, we implement a composite likelihood scheme to allow the method to scale to large sample sizes. We demonstrate the efficiency and accuracy of our method in a variety of benchmark tests using simulated data and present comparisons to other state-of-the-art methods. Specifically, our implementation using TMRCA as the latent variable shows comparable performance and provides accurate estimates of effective population sizes in intermediate and ancient times. Our method is agnostic to the phasing of the data, which makes it a promising alternative in scenarios where high quality data is not available, and has potential applications for pseudo-haploid data.

Estimating Personality Impression from Speech Record Using Hidden Markov Models

2015 ◽  
Vol 135 (12) ◽  
pp. 1517-1523 ◽  
Author(s):  
Yicheng Jin ◽  
Takuto Sakuma ◽  
Shohei Kato ◽  
Tsutomu Kunitachi
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov

Hidden Markov Processes

2014 ◽  
Author(s):  
M. Vidyasagar
Keyword(s):  
Hidden Markov Models ◽  
Markov Processes ◽  
Viterbi Algorithm ◽  
Markov Models ◽  
Hidden Markov ◽  
Local Alignment ◽  
Biological Applications ◽  
Standard Material ◽  
Hidden Markov Processes ◽  
Genomics And Proteomics

This book explores important aspects of Markov and hidden Markov processes and the applications of these ideas to various problems in computational biology. It starts from first principles, so that no previous knowledge of probability is necessary. However, the work is rigorous and mathematical, making it useful to engineers and mathematicians, even those not interested in biological applications. A range of exercises is provided, including drills to familiarize the reader with concepts and more advanced problems that require deep thinking about the theory. Biological applications are taken from post-genomic biology, especially genomics and proteomics. The topics examined include standard material such as the Perron–Frobenius theorem, transient and recurrent states, hitting probabilities and hitting times, maximum likelihood estimation, the Viterbi algorithm, and the Baum–Welch algorithm. The book contains discussions of extremely useful topics not usually seen at the basic level, such as ergodicity of Markov processes, Markov Chain Monte Carlo (MCMC), information theory, and large deviation theory for both i.i.d and Markov processes. It also presents state-of-the-art realization theory for hidden Markov models. Among biological applications, it offers an in-depth look at the BLAST (Basic Local Alignment Search Technique) algorithm, including a comprehensive explanation of the underlying theory. Other applications such as profile hidden Markov models are also explored.

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