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Stochastic motif extraction using hidden Markov model
1994
Proceedings. International Conference on Intelligent Systems for Molecular Biology
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In this paper, we study the application of an HMM (hidden Markov model) to the problem of representing protein sequences by a stochastic motif. ...
Using this method, we obtained an HMM for a leucine zipper motif. ...
HMMs for stochastical modeling and multiple alignment of globins. ...
pmid:7584381
fatcat:3gqhdpx6nzb2dnmh6zkbwefeey
Are the Hidden Markov Models Promising in Protein Research?
1993
Genome Informatics Series
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Hidden Markov Model (HMM), a type of stochastic model (signal source), is now becoming popular in molecular biology. ...
In the field of protein research, HMMs have been used to represent stochastic motifs of protein sequences, to model the structural patterns of protein, to predict the secondary structures and upper level ...
1Introduction A Hidden Markov Model (HMM) is a stochastic model of signal source. ...
doi:10.11234/gi1990.4.130
fatcat:kgzafnbg7nejpecnjx3tziwwly
Finding Genes by Hidden Markov Models with a Protein Motif Dictionary
1996
Genome Informatics Series
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A new method for combining protein motif dictionary to gene finding system is proposed. The system consists of Hidden Markov Models (HMMs) and a dictionary. ...
Using the same kind of technique of speech recognition by HMMs with a word dictionary and a grammar, the stochastic network of 'words' enables the motif dictionary to be used during the parsing of the ...
That is why hidden Markov models (HMM) are becoming widely used for gene recognition ( [9] {10][13] [14] ). ...
doi:10.11234/gi1990.7.88
fatcat:vdycwpuol5ebfgderwc5iofaki
Protein Motif Extraction Using Hidden Markov Model
1993
Genome Informatics Series
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A stochastic (protein) motif represents the portions of protein sequences that have a certain function or structure, where conditional probabilities are used to deal with the stochastic nature of the motif ...
In this paper, we study the application of HMM to the problem of representing protein sequences by a stochastic motif. ...
In this paper, we employ a stochastic motif using a Hidden Markov Model(HMM) to achieve high classification accuracy. ...
doi:10.11234/gi1990.4.56
fatcat:ogtv2ua7pbh6fmmh3iktxesdmu
A generative, probabilistic model of local protein structure
2008
Proceedings of the National Academy of Sciences of the United States of America
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The sequential dependencies along the chain are captured by using a dynamic Bayesian network (a generalization of a hidden Markov model), which emits angle pairs, amino acid labels, and secondary structure ...
Recently, we showed that a first-order Markov model forms an efficient probabilistic, generative model of the C␣ geometry of proteins in continuous space (9). ...
discussions on the angular distributions; Christopher Bystroff for help with HMMSTR and the newest version of I-sites; and the Bioinformatics Centre and the Zoological Museum, University of Copenhagen, for use ...
doi:10.1073/pnas.0801715105
pmid:18579771
pmcid:PMC2440424
fatcat:d5f36czmtrhdpilpsps2gsuywi
Motif Extraction: Normalization of Scores
1996
Genome Informatics Series
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This paper examines a method to normalize a score of a stochastic motif, represented by a hidden Markov model (HMM). ...
Introduction and Methods The stochastic motif deals with the stochastic nature or the sequence variety resulted from the evolution process, and is represented by an HMM which is commonly used in computational ...
For experiments, a leucine zipper motif is used. ...
doi:10.11234/gi1990.7.196
fatcat:os5fmujvxndmxgcgkq22irxcb4
A New Data Mining Approach for the Detection of Bacterial Promoters Combining Stochastic and Combinatorial Methods
2009
Journal of Computational Biology
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We present a new data mining method based on stochastic analysis (Hidden Markov Model [HMM]) and combinatorial methods for discovering new transcriptional factors in bacterial genome sequences. ...
Some selected motif consensuses were used as box1 (À35 motif ) in the search of a potential neighbouring box2 (À10 motif ) using a word enumeration algorithm. ...
C.E. and S.H. adapted the stochastic data mining methods, which were initially developed by J.F.M. ...
doi:10.1089/cmb.2008.0122
pmid:19772433
fatcat:dx5i4jnqw5dnbcib6bnmpmb66u
A Methodological Contribution to Music Sequences Analysis
[chapter]
2006
Lecture Notes in Computer Science
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The result is a set of likely themes and motifs. ...
The method relies on a pitch intervals representation of music and an event discovery system that extracts significant and repeated patterns from sequences. ...
The main idea is that of modeling each motif by means of a Profile Hidden Markov model (PHMM), and representing a sequence of motifs interleaved with gaps by a Hierarchical Hidden Markov model (HHMM). ...
doi:10.1007/11875604_47
fatcat:w56fbln6jbh7dmjia4povetpoi
Using Markov Models to Mine Temporal and Spatial Data
[chapter]
2011
New Fundamental Technologies in Data Mining
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These areas are then post-processed to extract some valuable knowledge from the data. Pattern matching : in this task, the GM measures the a posteriori probability P(model = someLabel/observedData). ...
The mining of temporal and / or spatial signals by graphical models can have several purposes: Segmentation : in this task, the GM clusters the signal into stationary (or homogeneous) and transient segments ...
Acknowledgments Many organizations had provided us with support and data. The genetic data mining work was supported by INRA, the région Lorraine and the ACI IMP-Bio initiative. ...
doi:10.5772/13720
fatcat:q3ym6eh6jzcljetkgayalqoppq
Quantifying information accumulation encoded in the dynamics of biochemical signaling
2021
Nature Communications
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We use it to understand NFκB transcriptional dynamics in response to different immune threats, and reveal that some threats are distinguished faster than others. ...
We employ a modeling approach to learn the ensemble of complex response trajectories using a time-inhomogeneous Markov model 30 or a hidden Markov model 31, 32 . ...
Here, the trajectory entropy is calculated by inferring a stochastic model, e.g., a hidden Markov model, from the data of biochemical signaling responses. ...
doi:10.1038/s41467-021-21562-0
pmid:33627672
pmcid:PMC7904837
fatcat:z6e7zcreu5ecnamudwc37yfxkq
GenRGenS: software for generating random genomic sequences and structures
2006
Bioinformatics
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It handles several classes of models useful for sequence analysis, such as Markov chains, hidden Markov models, weighted context-free grammars, regular expressions and PROSITE expressions. ...
GenRGenS also allows the user to combine several of these different models at the same time. ...
Stochastic context-free grammars (SCFGs) have long been used to model the structural properties of genomic sequences, particularly for predicting the structure of sequences or for searching for motifs ...
doi:10.1093/bioinformatics/btl113
pmid:16574695
fatcat:3w7zvylprzev5c4l2cg7e634om
Learning Profiles Based on Hierarchical Hidden Markov Model
[chapter]
2005
Lecture Notes in Computer Science
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User profile is encoded by means of a Hierarchical Hidden Markov Model (HHMM). The HHMM is a well formalized tool suitable to model complex patterns in long temporal or spatial sequences. ...
The Hierarchical Hidden Markov Model A Hierarchical Hidden Markov Model is a generalization of the Hidden Markov Model, which is a stochastic finite state automaton [11] defined by a tuple S, O, A, B ...
A formal framework to design and train complex HMMs is represented by the Hierarchical Hidden Markov Model (HHMM) [5] . ...
doi:10.1007/11425274_5
fatcat:sshygzdwvba33jjg4g7johtjea
Relevant Subsequence Detection with Sparse Dictionary Learning
[chapter]
2013
Lecture Notes in Computer Science
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Through experiments, we show that decompositions of sequence data induced by our RS-DL model can be eective both for discovering repeated patterns meaningful to humans and for extracting features useful ...
Sparse Dictionary Learning has recently become popular for discovering latent components that can be used to reconstruct elements in a dataset. ...
To understand Factorial Hidden Markov Models, one must rst understand the basic Hidden Markov Model (HMM), which denes a probability distribution over sequences. ...
doi:10.1007/978-3-642-40988-2_26
fatcat:jndvxh6srfbrbjg4e37bdmqpiy
Hidden Markov Model Approaches for Biological Studies
2017
Biometrics & Biostatistics International Journal
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This article will introduce the theory of hidden Markov model and the computational algorithms for the three fundamental statistical problems and summarize striking applications of hidden Markov models ...
The hidden Markov process is a class of doubly stochastic processes, characterized by Markov property and the output independence, in which an underlying Markov process is hidden, meaning the variable ...
The probabilistic model to characterize a hidden Markov process is referred to as a hidden Markov model (abbreviated as HMM). ...
doi:10.15406/bbij.2017.05.00139
fatcat:x5mqdr44gnbcteffu5g4ocl7c4
Time space stochastic modelling of agricultural landscapes for environmental issues
2013
Environmental Modelling & Software
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By means of stochastic models such as a hierarchical hidden Markov model and a Markov random field, ARPEnTAge performs an unsupervised clustering of a territory in order to reveal patches characterized ...
We present a time space modelling approach -and a generic software named ARPEnTAgecapable of clustering a territory based on its pluriannual land-use organization. ...
This paper is organized as follows : section 3 presents the stochastic models that ARPEnTAge implements : second-order Hidden Markov Models (HMM2), Hierarchical Hidden Markov Models (HHMM2), and Markov ...
doi:10.1016/j.envsoft.2013.03.014
fatcat:leclzkxrbzhr5jvwp566tqpdj4
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