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Splice Site Recognition Using Lower Dimensional LHMM Features and SVM Classifier
2017
International Journal for Research in Applied Science and Engineering Technology
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With an objective to further develop an efficient algorithm, Splice site recognition using lower dimensional Linear Hidden Markov Model (LHMM) features have been proposed in this paper. ...
At present, there are several algorithms available for splice site recognition with an aim to improve the prediction accuracy. ...
Hidden Markov Models (HMM) have been applied successfully in various applications, viz. Speech recognitions [11] . ...
doi:10.22214/ijraset.2017.11315
fatcat:o2kksdrvbfcebj6fyly5duuql4
A comparative genomic method for computational identification of prokaryotic translation initiation sites
2002
Nucleic Acids Research
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Our framework employs a product hidden Markov model (PROD-HMM) with state architecture to model the species-speci®c trinucleotide frequency patterns in sequences immediately upstream and downstream of ...
Depending on the intricacy of the features modeled by the hidden state architecture, intergenic, regulatory, promoter and coding regions can be delimited by this method. ...
We propose a new probabilistic method for prokaryotic genome annotation. The method employs a novel product hidden Markov model (PROD-HMM). ...
doi:10.1093/nar/gkf423
pmid:12136100
pmcid:PMC135744
fatcat:7acsgn2nkjft7ommwsretzxvre
Hidden Markov models from molecular dynamics simulations on DNA
2002
Proceedings of the National Academy of Sciences of the United States of America
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National Academy of Sciences is collaborating with JSTOR to digitize, preserve and extend access to ...
JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. ...
re] as described below. se( Hidden Markov Models. ...
doi:10.1073/pnas.132148699
pmid:12072566
pmcid:PMC124344
fatcat:atolfhnjt5dkjphi3s6kb2fweu
COMPUTATIONAL IDENTIFICATION OF PROMOTER REGIONS IN PROKARYOTES AND EUKARYOTES
2021
EPRA International Journal of Agriculture and Rural Economic Research
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In the post-genomics era, the availability of data makes it possible to build computational models to detect promoters robustly, because these models are expected to be helpful to academia and drug discovery ...
and prokaryotic promoters. ...
[14] In this article, the author shows that the hidden Markov model can learn the sequential structure that exists in both prokaryotic and eukaryotic promoter sequences. ...
doi:10.36713/epra7667
fatcat:wxqohvs3bbhftjv6gqkh7br4lq
DNA Structural Properties in the Classification of Genomic Transcription Regulation Elements
2012
Bioinformatics and Biology Insights
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This review is meant to provide an overview of the key aspects of these DNA conformational and physicochemical properties. ...
Eg, a higher-order dinucleotide sequence model is able to account for the dependency between two sequential base pairs. ...
The McPromoter method does this by dividing the promoter into smaller regions and models the average of the structural profiles in every segment as a single observation from a Hidden Markov Model. 61 ...
doi:10.4137/bbi.s9426
pmid:22837642
pmcid:PMC3399529
fatcat:qfodptjuyzaofpsotbk7qsamy4
Prediction of Genomic Functional Elements
2006
Annual review of genomics and human genetics (Print)
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As the number of sequenced genomes increases, the ability to deduce genome function becomes increasingly salient. ...
Recent methodologies for predicting noncoding RNA genes, including microRNA genes and their targets, are also reviewed. ...
In 1997 GENSCAN was published (26) and showed a significant improvement in gene prediction over the existing methods, using a generalized hidden Markov model to generate the gene structures. ...
doi:10.1146/annurev.genom.7.080505.115745
pmid:16824019
fatcat:ej3lgrqehvekxom736ekx6ob4m
Markov models of genome segmentation
2007
Physical Review E
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Higher-order Markov models are more sensitive to the details of local patterns and in application to genome analysis, this makes it possible to segment a sequence at positions that are biologically meaningful ...
We introduce Markov models for segmentation of symbolic sequences, extending a segmentation procedure based on the Jensen-Shannon divergence that has been introduced earlier. ...
R.R. would like to acknowledge the hospitality of the Institute for Advanced Study, Princeton, where this work was begun. ...
doi:10.1103/physreve.75.011915
pmid:17358192
fatcat:tqzcfbgyyvdlvh3ki7de6npfwa
The future of transposable element annotation and their classification in the light of functional genomics - what we can learn from the fables of Jean de la Fontaine?
2016
Mobile Genetic Elements
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Our hope is to encourage the formation of a society to organize a larger debate on these questions and to promote the adoption of standards for annotation and an improved TE classification. ...
Today, whole genome TE annotation is mostly done using tools that were developed to aid gene annotation rather than to specifically study TEs. ...
The authors of RepeatMasker have attempted to address the third problematic issue (very divergent repeats) by introducing Dfam 11 which uses hidden Markov models and sequence alignments instead of simple ...
doi:10.1080/2159256x.2016.1256852
pmid:28090383
pmcid:PMC5160393
fatcat:mq56vw6f6jaxniziizayx7zdxq
Mycological Assessment of Deteriorated Lycopersicum esculentum Fruits Sold in Wukari Nigeria
2020
Journal of Biotechnology Research
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Some of these organisms are known to be human pathogens, hence care must be taken when handling and using deteriorated tomatoes to avoid infections from these organisms. ...
However, high water content makes them more prone to microbial attack especially spoilage by fungi. ...
The promoter prediction is based on PWM models and Hidden Markov Models (HMM) of motifs (-35 and -10 regions). ...
doi:10.32861/jbr.67.84.89
fatcat:qkblhkjxyrgfbk6rtdj24zokre
Hybrid Approach Using SVM and MM2 in Splice Site Junction Identification
2014
Current Bioinformatics
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Hence, an efficient method, MM2F-SVM is proposed through this article, which consists of three stages -initial stage, in which a second order Markov Model (MM2) is used, i.e. feature extraction; intermediate ...
Therefore, the algorithms used in the splice sites identification must be improved in order to recover the prediction accuracy. ...
ACKNOWLEDGEMENTS The authors are grateful to the anonymous reviewers, Prof. Sanghamitra Bandyopadhyay, Machine Intelligence Unit, Indian Statistical Institute, Kolkata and Prof. ...
doi:10.2174/1574893608999140109121721
fatcat:lc37zxp55je65bybzgwwsncsyu
Comprehensive identification and quantification of microbial transcriptomes by genome-wide unbiased methods
2011
Current Opinion in Biotechnology
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Providing comprehensive identification and quantification of transcripts with an unprecedented resolution, they are leading to major breakthroughs in systems biology. ...
Methodological requirements and statistical frameworks are often similar in both the approaches despite differences in the nature of the data. ...
The idea has been successfully employed for tiling array data, from simple piecewise-constant regression [42,43 ,44 ] to more sophisticated hidden Markov models which integrate signal drifts [45 ] . ...
doi:10.1016/j.copbio.2010.10.003
pmid:21074401
fatcat:5nzupyyb4rfipah6mt67jiashm
Mosaic origin of the eukaryotic kinetochore
2019
Proceedings of the National Academy of Sciences of the United States of America
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While prokaryotes operate simple systems to connect DNA to the segregation machinery during cell division, eukaryotes use a highly complex protein assembly known as the kinetochore. ...
The emergence of eukaryotes from ancient prokaryotic lineages embodied a remarkable increase in cellular complexity. ...
We are indebted to the members of the G.J.P.L.K. and B.S. labs for helpful discussions on the research. ...
doi:10.1073/pnas.1821945116
pmid:31127038
pmcid:PMC6601020
fatcat:aan7jalhyfha7jjwq2mbjkend4
Computational Identification of Novel Genes: Current and Future Perspectives
2016
Bioinformatics and Biology Insights
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Their discovery called for an extension of the historical hypotheses about gene origination. ...
Computational approaches are further prime methods that can be based on existing models or leveraging biological evidences from experiments. ...
by probabilistic models, namely, hidden Markov models (HMMs). ...
doi:10.4137/bbi.s39950
pmid:27493475
pmcid:PMC4970615
fatcat:6rbtbdxmf5hz5nthw6hgesrl3m
An integrative computational approach to effectively guide experimental identification of regulatory elements in promoters
2012
BMC Bioinformatics
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We apply it to promoters that drive tumor-specific gene expression in tumor-colonizing Gram-negative bacteria. ...
Conclusions: Experimental analysis of promoter structures guided by bioinformatics has proved to be efficient. ...
Hidden Markov Model prediction shows lower specificity (C TSS + = 0.83, C TSS -= 0.43). ...
doi:10.1186/1471-2105-13-202
pmid:22897887
pmcid:PMC3465240
fatcat:6dtgu3qe7rgvvlejhvq3jsmkwy
MRFy: Remote Homology Detection for Beta-Structural Proteins Using Markov Random Fields and Stochastic Search
2015
IEEE/ACM Transactions on Computational Biology & Bioinformatics
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2013 pre-print arXiv:1304.6476v1 fatcat:xab2owzi2nh7vephhybethrz34
Menke, Berger, and Cowen proposed a Markov random field model to predict remote homology for beta-structural proteins, but their formulation was computationally intractable on many beta-strand topologies ...
An automatic computational method reasonably approximates a human-curated hierarchical organization of proteins according to their degree of homology. ...
[KBM + 94] used HMMs to model protein evolution.1.3.1 Profile Hidden Markov ModelsWith respect to homology detection, profile hidden Markov models have been popular. ...
doi:10.1109/tcbb.2014.2344682
pmid:26357074
fatcat:6wo3cmou5jcqdhd4bm7hpm634m
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