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Gene Prediction [chapter]

Tyler Alioto
2012 Msphere  
There are four possibilities for the way they overlap: Outline • Algorithms ○ Markov Chains ○ Hidden Markov Model ○ Interpolated Markov model • Learning Methods ○ Supervised ○ Unsupervised  ...  Unsupervised: The algorithm does not require a predetermined training set to estimate parameters Outline • Algorithms ○ Markov Chains ○ Hidden Markov Model ○ Interpolated Markov model • Learning  ...  Types of Blast Non-coding RNA • The secondary structures of rRNA and tRNA are highly conserved. • The accuracy of RNA similarity searching is much improved by including secondary structure elements.  ... 
doi:10.1007/978-1-61779-582-4_6 pmid:22407709 fatcat:v7n67td27zagreqczqim27b7te

A Novel Approach for Protein Structure Prediction [article]

Saurabh Sarkar, Prateek Malhotra, Virender Guman
2012 arXiv   pre-print
In second model we have taken protein sequences as hidden and protein structures as observed. The efficiencies for both the hidden markov models have been calculated.  ...  In this context we have assumed two hidden markov models. In first model we have taken protein secondary structures as hidden and protein sequences as observed.  ...  These two independent predictions allow one to optimize the parameters calculated over the secondary structure database to provide the final prediction of secondary structure.  ... 
arXiv:1206.3509v1 fatcat:pwjphhcaw5akjofrojt4jzxiu4

Bayesian Protein Secondary Structure Prediction With Near-Optimal Segmentations

Zafer Aydin, Yucel Altunbasak, Hakan Erdogan
2007 IEEE Transactions on Signal Processing  
Index Terms-Hidden semi-Markov model, N-best list, protein secondary structure prediction, single-sequence prediction, stack decoder, suboptimal segmentations.  ...  In this paper, we introduce an alternative decoding technique for the hidden semi-Markov model (HSMM) originally employed in the BSPSS algorithm, and further developed in the IPSSP algorithm.  ...  In this paper, we developed two approximate N-best algorithms for protein secondary structure prediction that employ hidden semi-Markov models.  ... 
doi:10.1109/tsp.2007.894404 fatcat:5ugrlfi4qvcfzhyhuqv5lrwpuu

Are the Hidden Markov Models Promising in Protein Research?

Kiyoshi Asai, Hidetoshi Tanaka, Katunobu Itou, Kentaro Onizuka
1993 Genome Informatics Series  
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  ...  Hidden Markov Model (HMM), a type of stochastic model (signal source), is now becoming popular in molecular biology.  ...  1Introduction A Hidden Markov Model (HMM) is a stochastic model of signal source.  ... 
doi:10.11234/gi1990.4.130 fatcat:kgzafnbg7nejpecnjx3tziwwly

Analysis of an optimal hidden Markov model for secondary structure prediction

Juliette Martin, Jean-François Gibrat, François Rodolphe
2006 BMC Structural Biology  
We start by analyzing the model features and see how it offers a new vision of local structures. We then use it for secondary structure prediction.  ...  The hidden Markov model presented here achieves valuable prediction results using only a limited number of parameters.  ...  We thank Claire Guillet and Emeline Legros for their contribution to the work presented in additional file 4. We are grateful to INRA for awarding a Fellowship to JM.  ... 
doi:10.1186/1472-6807-6-25 pmid:17166267 pmcid:PMC1769381 fatcat:nanhwhek6zebbobxl5woliswgq

Hidden Markov model and Chapman Kolmogrov for protein structures prediction from images

Md. Sarwar Kamal, Linkon Chowdhury, Mohammad Ibrahim Khan, Amira S. Ashour, João Manuel R.S. Tavares, Nilanjan Dey
2017 Computational biology and chemistry  
Finally, Hidden Markov model and Chapman Kolmogrov equation are applied on the classified structures for predicting the protein structure.  ...  Several protein structure prediction methods use neural network (NN). However, the Hidden Markov model is more interpretable and effective for more biological data analysis compared to the NN.  ...  Another effective research methodology for secondary structure prediction is the Hidden Markov Models (HMM) .These models show their ability by allowing an explicit modeling of the data. Asai et al.  ... 
doi:10.1016/j.compbiolchem.2017.04.003 pmid:28432981 fatcat:t5alpi5xjngfjkbl7dr2z6zfvi

A Modified Hidden Markov Model and Its Application in Protein Secondary Structure Prediction

Sima Naghizadeh, Vahid Rezaeitabar, Hamid Pezeshk
2012 Journal of Proteomics & Bioinformatics  
One of the important tools in analyzing and modeling biological data is the Hidden Markov Model (HMM), which is used for gene prediction, protein secondary structure and other essential tasks.  ...  Citation: Naghizadeh S, Rezaeitabar V, Pezeshk H, Matthews D (2012) A Modified Hidden Markov Model and Its Application in Protein Secondary Structure Prediction. J Proteomics Bioinform 5: 024-030.  ...  Amir Momen-Roknabadi for providing the dataset and Miss Nasim Ejlali for helpful comments on an earlier draft of this paper.  ... 
doi:10.4172/jpb.1000209 fatcat:pqft43kqrnfkdj3lwuzpxhmltu

Methods for Protein Structure Prediction and Its Application in Drug Design Using Hidden Markov Model

Nidhi Katiyar
2016 INTERNATIONAL JOURNAL OF EMERGING TRENDS IN SCIENCE AND TECHNOLOGY  
For prediction the new drug structure many methods are used like artificial neural networks (ANN), fuzzy neural networks and hidden Markov Model (HMM).  ...  Keywords: Structure based drug design (SBDD); Hidden Markov Model (HMM), Protein structure prediction (PSP); Drug Design (DD); 3-dimensional structure (3DS)  ...  Hidden Markov Model A Hidden Markov Model (HMM) is a stochastic model that uses the statistical properties of observed real world data.  ... 
doi:10.18535/ijetst/v3i03.09 fatcat:lkkou55pvfbzpecnq5qsx5btwu

Hidden Markov Model in Biological Sequence Analysis– A Systematic Review

2016 International Journal of Scientific and Innovative Mathematical Research  
For biological sequence analysis Hidden Markov Model (HMM) have been used widely in many applications. It has provided solution for various biological sequence analysis problems.  ...  This paper especially focusing on HMM and its various types like Profile Hidden Markov Models (PHMMs) and Pair Hidden Markov Models (Pair HMM).  ...  Asai et.al (1993) [5] introduced the prediction system of protein secondary structure by HMM.  ... 
doi:10.20431/2347-3142.0403001 fatcat:bauj4ftia5dzjps3bdwmrskg3e

Predicting Secondary Structure of All-Helical Proteins Using Hidden Markov Support Vector Machines [chapter]

Blaise Gassend, Charles W. O'Donnell, William Thies, Andrew Lee, Marten van Dijk, Srinivas Devadas
2006 Lecture Notes in Computer Science  
Our goal is to develop a state-of-the-art secondary structure predictor with an intuitive and biophysically-motivated energy model through the use of Hidden Markov Support Vector Machines (HM-SVMs), a  ...  We focus on the prediction of alpha helices and show that by using HM-SVMs, a simple 7-state HMM with 302 parameters can achieve a Qα value of 77.6% and a SOVα value of 73.4%.  ...  Acknowledgements We thank Chris Batten, Edward Suh and Rodric Rabbah for their early contributions to this work, and the anonymous reviewers for their helpful comments.  ... 
doi:10.1007/11818564_11 fatcat:m6ia7g2h3zd4bgspgfbvvsqr7a

Five Hierarchical Levels of Sequence-Structure Correlation in Proteins

Christopher Bystroff, Yu Shao, Xin Yuan
2004 Applied Bioinformatics  
The I-sites Library (folding Initiation sites) models local structure motifs. HMMSTR (Hidden Markov Model for STRucture) is a hidden Markov model for extended motifs.  ...  Here we review statistical models, including sequence profiles, hidden Markov models and interaction potentials, for the first four levels of structural detail.  ...  using HMMSTR. 4 SCALI-HMM: a hidden Markov model for protein structure core alignment.  ... 
doi:10.2165/00822942-200403020-00004 pmid:15693735 fatcat:tiis4eilyfhthlx4in6vnrbzou

Opportunistic Channel Access Algorithm Based on Hidden Semi Markov Model for Cognitive Radio Networks

B. Senthil Kumar, Dr.S.K. Srivatsa
2014 Bonfring International Journal of Research in Communication Engineering  
Hidden markov model is used for probability calculation of primary user state and the predicted channel is validated using the proposed quality estimation method.  ...  In recent years, Hidden Markov Model(HMM) has been considered as a powerful statistical tool for modeling generative sequences that can be characterised by an underlying process generating an observable  ...  METHODOLOGY Hidden Semi Markov Model A hidden semi markov model is an extension of hidden markov model using the semi markov chain process with parameters such as variable duration or sojourn time for  ... 
doi:10.9756/bijrce.8098 fatcat:q7hf34xplveihamkn5dpkptawi

Hidden Markov Models and their Applications in Biological Sequence Analysis

Byung-Jun Yoon
2009 Current Genomics  
Hidden Markov models (HMMs) have been extensively used in biological sequence analysis.  ...  Index Terms Hidden Markov model (HMM), pair-HMM, profile-HMM, context-sensitive HMM (csHMM), profile-csHMM, sequence analysis.  ...  For example, the PHMMTSs (pair hidden Markov models on tree structures) extend the pair-HMMs so that we can use them for aligning trees [50] .  ... 
doi:10.2174/138920209789177575 pmid:20190955 pmcid:PMC2766791 fatcat:h6m33ccye5h27hdzk6z27khpki

Classification of Proteins via Successive State Splitting Algorithm of Hidden Markov Network

Hidetoshi Tanaka, Kentaro Onizuka, Kiyoshi Asai
1993 Genome Informatics Series  
Hidden Markov Model (HMM) introduces a stochastic approach to protein representation and motif abstraction.  ...  It uses no previous knowledge of the proteins. The SSS algorithm was originally developed for allophone modeling. It is based on continuous distribution of phenome data.  ...  Concluding Remarks The SSS algorithm enables to obtain an appropriate Hidden Markov Network automatically, and the Hidden Markov Model simultaneously.  ... 
doi:10.11234/gi1990.4.224 fatcat:ksmfrwwmhrg3jl35u3wnnpt7xm

An information theoretic approach for improving data driven prediction of protein model quality

Alfonso Montuori, Giovanni Raimondo, Eros Pasero
2008 Computers and Mathematics with Applications  
We present the results of an information theory-based approach to select an optimal subset of features for the prediction of protein model quality.  ...  The optimal subset of features was calculated by means of a backward selection procedure.  ...  Acknowledgments We would like to thank the authors of the programs used for the scientific research related to this paper.  ... 
doi:10.1016/j.camwa.2006.12.096 fatcat:ola4xfwdxnhqtcbyogwv6h3hna
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