A TUTORIAL ON MARKOV MODELS BASED ON MENDEL'S CLASSICAL EXPERIMENTS

2005 ◽  
Vol 03 (06) ◽  
pp. 1441-1460 ◽  
Author(s):  
STEINAR THORVALDSEN
Keyword(s):  
Nucleic Acids ◽  
Hidden Markov Models ◽  
Prior Knowledge ◽  
Probabilistic Model ◽  
Markov Models ◽  
Hidden Markov ◽  
Biological Sequences ◽  
Dual Nature ◽  
Background Theory ◽  
Series Of Experiments

Hidden Markov Models (HMM) can be extremely useful tools for the analysis of data from biological sequences, and provide a probabilistic model of protein families. Most reviews and general introductions follow the excellent tutorial by Rabiner,1 where the focus is outside biology. Mendel's famous experiments in plant hybridisation were published in 1866 and are often considered the icebreaking work of modern genetics. He had no prior knowledge of the dual nature of genes, but through a series of experiments he was able to anticipate the hidden concept and name it "Elemente". In this paper we present the background, theory and algorithms of HMM based on examples from Mendel's experiments, and introduce the toolbox "mendelHMM". This approach is considered to have some intuitive advantages in a biological and bioinformatical setting. Applications to analysing bio-sequences like nucleic acids and proteins are also discussed.

Degradation Assessment for Machinery Prognostics Using Hidden Markov Models

2005 ◽  
Author(s):  
Hai Qiu ◽  
Haitao Liao ◽  
Jay Lee
Keyword(s):  
Hidden Markov Models ◽  
Prior Knowledge ◽  
Probabilistic Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Degradation Process ◽  
Full Understanding ◽  
Degradation Processes ◽  
Degradation Pattern ◽  
Detection And Recognition

Degradation detection and recognition of degradation pattern are crucial to the successful deployment of prognostics. A machine degradation process is known to be stochastic instead of deterministic. Recognizing the degradation pattern needs helps from stochastic and probabilistic models. Among various stochastic approaches. Hidden Markov Models (HMMs) have been proven to be very effective in modeling both dynamic and static signals [1]. In this paper, aiming to providing a guideline of how to effectively and efficiently use the HMMs to assess degradation for various machinery prognostic applications, three different approaches of applying the HMMs are reviewed and compared. It demonstrates that depending on the varieties of applications, available prior knowledge, and characteristics of degradation processes, those three implementation approaches perform differently. A full understanding of the strengths and weaknesses of each deployment approach is extremely important in order to effectively utilize this powerful tool for system degradation assessment.

Quality Monitoring of Surface Roughness and Roundness Using Hidden Markov Model

2012 ◽  
Vol 630 ◽  
pp. 308-314
Author(s):  
Kang He ◽  
Min Ping Jia ◽  
Zhuan Zhe Zhao
Keyword(s):  
Surface Roughness ◽  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Recognition Rate ◽  
Vibration Energy ◽  
Vibration Displacement ◽  
Cutting Vibration ◽  
Series Of Experiments ◽  
Judgment Matrix

The surface roughness and roundness(SRR) are widely used indexes of mechanical product quality. How to implement the SRR monitoring is a crucial task. In this study, the hidden Markov models (HMMs) and the cutting vibration signals are applied to monitor the SRR in variant cutting conditions. Unlike most of the prior work only to reveal one element of the geometric specifications, based on the theoretical analysis of the influence of tool vibration displacement on the SRR, the vibration energy characteristic(VEC) is determined to serve as the characteristic for monitoring surface roughness(Ra) and roundness(Rd) synchronously.Which make up the insufficiency of the comprehensive monitoring of workpiece quality. Moreover, although classical hidden Markov models (HMMs) have been successfully used for fault diagnostics of mechanical systems, this method based on recognition rate is becoming unreliable to monitor the accuracy of the workpiece. Hence, the HMM-based judgment matrix method is proposed and it is tested and validated successfully using for SRR monitoring through a series of experiments.

scholarly journals Specialized Hidden Markov Model Databases for Microbial Genomics

2003 ◽  
Vol 4 (2) ◽  
pp. 250-254 ◽  
Author(s):  
Martin Gollery
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Standard Procedure ◽  
Biological Sequences ◽  
Microbial Genomics ◽  
Size Number ◽  
Large Groups

As hidden Markov models (HMMs) become increasingly more important in the analysis of biological sequences, so too have databases of HMMs expanded in size, number and importance. While the standard paradigm a short while ago was the analysis of one or a few sequences at a time, it has now become standard procedure to submit an entire microbial genome. In the future, it will be common to submit large groups of completed genomes to run simultaneously against a dozen public databases and any number of internally developed targets. This paper looks at some of the readily available HMM (or HMM-like) algorithms and several publicly available HMM databases, and outlines methods by which the reader may develop custom HMM targets.

scholarly journals A new algorithm to train hidden Markov models for biological sequences with partial labels

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Jiefu Li ◽  
Jung-Youn Lee ◽  
Li Liao
Keyword(s):  
Active Learning ◽  
Hidden Markov Models ◽  
Markov Models ◽  
De Novo ◽  
Sequence Data ◽  
Hidden Markov ◽  
Biological Sequences ◽  
Training Method ◽  
Functional Protein ◽  
Ongoing Research

Abstract Background Hidden Markov models (HMM) are a powerful tool for analyzing biological sequences in a wide variety of applications, from profiling functional protein families to identifying functional domains. The standard method used for HMM training is either by maximum likelihood using counting when sequences are labelled or by expectation maximization, such as the Baum–Welch algorithm, when sequences are unlabelled. However, increasingly there are situations where sequences are just partially labelled. In this paper, we designed a new training method based on the Baum–Welch algorithm to train HMMs for situations in which only partial labeling is available for certain biological problems. Results Compared with a similar method previously reported that is designed for the purpose of active learning in text mining, our method achieves significant improvements in model training, as demonstrated by higher accuracy when the trained models are tested for decoding with both synthetic data and real data. Conclusions A novel training method is developed to improve the training of hidden Markov models by utilizing partial labelled data. The method will impact on detecting de novo motifs and signals in biological sequence data. In particular, the method will be deployed in active learning mode to the ongoing research in detecting plasmodesmata targeting signals and assess the performance with validations from wet-lab experiments.

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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