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Stochastic motif extraction using hidden Markov model

Y Fujiwara, M Asogawa, A Konagaya
1994 Proceedings. International Conference on Intelligent Systems for Molecular Biology  
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?

Kiyoshi Asai, Hidetoshi Tanaka, Katunobu Itou, Kentaro Onizuka
1993 Genome Informatics Series  
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

Kiyoshi Asai, Tetsushi Yada, Katunobu Itou
1996 Genome Informatics Series  
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

Yukiko Fujiwara, Akihiko Konagaya
1993 Genome Informatics Series  
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

Wouter Boomsma, Kanti V. Mardia, Charles C. Taylor, Jesper Ferkinghoff-Borg, Anders Krogh, Thomas Hamelryck
2008 Proceedings of the National Academy of Sciences of the United States of America  
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

Y. Fujiwara, M. Asogawa
1996 Genome Informatics Series  
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

Catherine Eng, Charu Asthana, Bertrand Aigle, Sébastien Hergalant, Jean-François Mari, Pierre Leblond
2009 Journal of Computational Biology  
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]

Daniele P. Radicioni, Marco Botta
2006 Lecture Notes in Computer Science  
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]

Jean-Franois Mari, Florence Le, El Ghali, Marc Benot, Catherine Eng, Annabelle Thibessard, Pierre Leblo
2011 New Fundamental Technologies in Data Mining  
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

Ying Tang, Adewunmi Adelaja, Felix X.-F. Ye, Eric Deeds, Roy Wollman, Alexander Hoffmann
2021 Nature Communications  
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

Y. Ponty, M. Termier, A. Denise
2006 Bioinformatics  
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]

Ugo Galassi, Attilio Giordana, Lorenza Saitta, Maco Botta
2005 Lecture Notes in Computer Science  
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]

Sam Blasiak, Huzefa Rangwala, Kathryn B. Laskey
2013 Lecture Notes in Computer Science  
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

Xiang Yang Lou
2017 Biometrics & Biostatistics International Journal  
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

Jean François Mari, El Ghali Lazrak, Marc Benoît
2013 Environmental Modelling & Software  
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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