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Relevant bibliographies by topics / DNA Sequence Classification

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Academic literature on the topic 'DNA Sequence Classification'

Author: Grafiati

Published: 4 June 2021

Last updated: 10 February 2022

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Journal articles on the topic "DNA Sequence Classification"

1

Pandey, Subhash Chandra, and Saket Kumar Singh. "DNA sequence based data classification technique." CSI Transactions on ICT 3, no. 1 (2015): 59–69. http://dx.doi.org/10.1007/s40012-015-0072-x.

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WANG, JASON T. L., STEVE ROZEN, BRUCE A. SHAPIRO, DENNIS SHASHA, ZHIYUAN WANG, and MAISHENG YIN. "New Techniques for DNA Sequence Classification." Journal of Computational Biology 6, no. 2 (1999): 209–18. http://dx.doi.org/10.1089/cmb.1999.6.209.

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Nguyen, Ngoc Giang, Vu Anh Tran, Duc Luu Ngo, et al. "DNA Sequence Classification by Convolutional Neural Network." Journal of Biomedical Science and Engineering 09, no. 05 (2016): 280–86. http://dx.doi.org/10.4236/jbise.2016.95021.

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Lee, Dong Wook, and Kwee-Bo Sim. "Negative Selection Algorithm for DNA Sequence Classification." International Journal of Fuzzy Logic and Intelligent Systems 4, no. 2 (2004): 231–35. http://dx.doi.org/10.5391/ijfis.2004.4.2.231.

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Biedrzycki, Rafał, and Jarosław Arabas. "KIS: An automated attribute induction method for classification of DNA sequences." International Journal of Applied Mathematics and Computer Science 22, no. 3 (2012): 711–21. http://dx.doi.org/10.2478/v10006-012-0053-2.

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Abstract This paper presents an application of methods from the machine learning domain to solving the task of DNA sequence recognition. We present an algorithm that learns to recognize groups of DNA sequences sharing common features such as sequence functionality. We demonstrate application of the algorithm to find splice sites, i.e., to properly detect donor and acceptor sequences. We compare the results with those of reference methods that have been designed and tuned to detect splice sites. We also show how to use the algorithm to find a human readable model of the IRE (Iron-Responsive Ele

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Zheng, Zhiming, and Ya Wang. "DNA binding proteins: outline of functional classification." BioMolecular Concepts 2, no. 4 (2011): 293–303. http://dx.doi.org/10.1515/bmc.2011.023.

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AbstractDNA-binding proteins composed of DNA-binding domains directly affect genomic functions, mainly by performing transcription, DNA replication or DNA repair. Here, we briefly describe the DNA-binding proteins according to these three major functions. Transcription factors that usually bind to specific sequences of DNA could be classified based on their sequence similarity and the structure of the DNA-binding domains, such as basic, zinc-coordinating, helix-turn-helix domains, etc. Most DNA replication factors do not need a specific sequence of DNA, but instead mainly depend on a DNA struc

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Gunasekaran, Hemalatha, K. Ramalakshmi, A. Rex Macedo Arokiaraj, S. Deepa Kanmani, Chandran Venkatesan, and C. Suresh Gnana Dhas. "Analysis of DNA Sequence Classification Using CNN and Hybrid Models." Computational and Mathematical Methods in Medicine 2021 (July 15, 2021): 1–12. http://dx.doi.org/10.1155/2021/1835056.

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In a general computational context for biomedical data analysis, DNA sequence classification is a crucial challenge. Several machine learning techniques have used to complete this task in recent years successfully. Identification and classification of viruses are essential to avoid an outbreak like COVID-19. Regardless, the feature selection process remains the most challenging aspect of the issue. The most commonly used representations worsen the case of high dimensionality, and sequences lack explicit features. It also helps in detecting the effect of viruses and drug design. In recent days,

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Valiunas, Deividas, Rasa Jomantiene та Robert Edward Davis. "Evaluation of the DNA-dependent RNA polymerase β-subunit gene (rpoB) for phytoplasma classification and phylogeny". International Journal of Systematic and Evolutionary Microbiology 63, Pt_10 (2013): 3904–14. http://dx.doi.org/10.1099/ijs.0.051912-0.

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Phytoplasmas are classified into 16Sr groups and subgroups and ‘Candidatus Phytoplasma ’ species, largely or entirely based on analysis of 16S rRNA gene sequences. Yet, distinctions among closely related ‘Ca. Phytoplasma ’ species and strains based on 16S rRNA genes alone have limitations imposed by the high degree of rRNA nucleotide sequence conservation across diverse phytoplasma lineages and by the presence in a phytoplasma genome of two, sometimes sequence-heterogeneous, copies of the 16S rRNA gene. Since the DNA-dependent RNA polymerase (DpRp) β-subunit gene (rpoB) exists as a single copy

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Marrero, Glorimar, Kevin L. Schneider, Daniel M. Jenkins, and Anne M. Alvarez. "Phylogeny and classification of Dickeya based on multilocus sequence analysis." International Journal of Systematic and Evolutionary Microbiology 63, Pt_9 (2013): 3524–39. http://dx.doi.org/10.1099/ijs.0.046490-0.

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Bacterial heart rot of pineapple reported in Hawaii in 2003 and reoccurring in 2006 was caused by an undetermined species of Dickeya . Classification of the bacterial strains isolated from infected pineapple to one of the recognized Dickeya species and their phylogenetic relationships with Dickeya were determined by a multilocus sequence analysis (MLSA), based on the partial gene sequences of dnaA, dnaJ, dnaX, gyrB and recN. Individual and concatenated gene phylogenies revealed that the strains form a clade with reference Dickeya sp. isolated from pineapple in Malaysia and are closely related

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Sohsah, Gihad N., Ali Reza Ibrahimzada, Huzeyfe Ayaz, and Ali Cakmak. "Scalable classification of organisms into a taxonomy using hierarchical supervised learners." Journal of Bioinformatics and Computational Biology 18, no. 05 (2020): 2050026. http://dx.doi.org/10.1142/s0219720020500262.

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Accurately identifying organisms based on their partially available genetic material is an important task to explore the phylogenetic diversity in an environment. Specific fragments in the DNA sequence of a living organism have been defined as DNA barcodes and can be used as markers to identify species efficiently and effectively. The existing DNA barcode-based classification approaches suffer from three major issues: (i) most of them assume that the classification is done within a given taxonomic class and/or input sequences are pre-aligned, (ii) highly performing classifiers, such as SVM, ca

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Dissertations / Theses on the topic "DNA Sequence Classification"

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Becker, Nils B. "Sequence dependent elasticity of DNA." Doctoral thesis, [S.l. : s.n.], 2007. http://nbn-resolving.de/urn:nbn:de:swb:14-1186511923001-71752.

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Mungre, Surbhi. "LDA-based dimensionality reduction and domain adaptation with application to DNA sequence classification." Thesis, Kansas State University, 2011. http://hdl.handle.net/2097/8846.

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Master of Science<br>Department of Computing and Information Sciences<br>Doina Caragea<br>Several computational biology and bioinformatics problems involve DNA sequence classification using supervised machine learning algorithms. The performance of these algorithms is largely dependent on the availability of labeled data and the approach used to represent DNA sequences as {\it feature vectors}. For many organisms, the labeled DNA data is scarce, while the unlabeled data is easily available. However, for a small number of well-studied model organisms, large amounts of labeled data are availab

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Gilmore, Luther Martin. "DNA sequence analysis of the rDNA ITS 1/2 region in the evolutionary delineation of the Pilobolaceae." Virtual Press, 2006. http://liblink.bsu.edu/uhtbin/catkey/1348863.

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This project represents an initial examination of the evolutionary history of the genus Pilobolus, and provides a starting point for developing a method to accurately classify members of this genus using molecular genetics. The data analysis presented in this paper suggests a potential evolutionary path in which P. umbonatus and P. sphaerosporus diverged (along a common path) from an original generic ancestor early, but independently, from the evolutionary course taken by P. kleinii. Both Maximum Likelihood and parsimony analyses concur in the branch patterns. Additionally, formation of specie

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Seiffert, Jan. "Ein Sequenzdesign-Algorithmus für verzweigte DNA-Strukturen." Doctoral thesis, Technische Universität Dresden, 2007. https://tud.qucosa.de/id/qucosa%3A23921.

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Aufgrund ihrer Selbstorganisationseigenschaften besitzt DNA ein großes Potential für den Einsatz in Bottom-up-Techniken der Nanotechnologie. So erlaubt DNA eine genau definierte Anordnung von Bauelementen im Abstand von nur wenigen Nanometern. Zum Beispiel kann ein regelmäßig mit Metallclustern oder Proteinen bestücktes DNA-Netz als Katalysator oder in Sensoren eingesetzt werden. DNA wird außerdem als Templat für Nanodrähte benutzt und kann deshalb eine wichtige Rolle in einer zukünftigen Nanoelektronik spielen. DNA-Strukturen entstehen meist durch Selbsassemblierung von Einzelstrangmolekülen

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De, Villiers Margaret J. (Margaret Jenifer). "Molecular systematics of the Western Cape genus Serruria Salisb. (Proteaceae L.) based on DNA sequence data." Thesis, Stellenbosch : Stellenbosch University, 2004. http://hdl.handle.net/10019.1/50030.

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Thesis (MSc)--University of Stellenbosch, 2004.<br>ENGLISH ABSTRACT: The Cape Floristic Region (CFR) is situated at the southern tip of Africa and possesses a flora that is unique amongst the floras of the rest of the world, both in terms of its incredibly high species richness, and its high levels of endemism. Proteaceae, the family to which Serruria belongs, is widely distributed amongst the landmasses of the southern hemisphere, with its centres of diversity occurring in Australia and southern Africa. Previous molecular and morphological analyses performed on the South African subfami

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Benke, Annegret. "Aufbau nanoskopischer Netzwerke aus DNA und Bindeproteinen." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2007. http://nbn-resolving.de/urn:nbn:de:swb:14-1194889063905-84093.

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Zusammenfassung Die vorliegende Arbeit beschäftigt sich mit Grundlagenuntersuchungen zum Aufbau von nanoskopischen Netzwerken aus DNA. Dabei werden zwei Wege verfolgt: Das Stempeln von DNA-Molekülen auf ein Substrat und die Herstellung von Verknüpfungen aus DNA mit Hilfe von Bindeproteinen. Stempeln von DNA-Molekülen In dieser Arbeit wurde ein Beitrag zu den materialwissenschaftlichen Grundlagen des Übertragens von DNA mit der Stempel-Technik erbracht. Hierbei wurden sowohl das Beladen des Stempels durch Molecular Combing als auch die Übertragung der Moleküle durch Transfer Printing unter den

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Kick, Alfred. "Oberflächenplasmonenresonanz-basierte DNA-Chips und Nucleobasen-Sequenzentwurf." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2013. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-126339.

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Die vorliegende Dissertation beschreibt die Erarbeitung anwendbarer Methoden zum Aufbau Oberflächenplasmonenresonanz (SPR)-basierter DNA-Mikroarrays. Es werden die Beziehungen zwischen allen Teilschritten der Entwicklung eines DNA-Biosensors aufgezeigt. Die Sondendichte auf der Sensoroberfläche ist entscheidend für die Leistungsfähigkeit eines DNA-Chips. In dieser Arbeit werden thiolmodifizierte Sonden und solche mit Phosphorothioatgruppen verwendet und verglichen. Der Aufbau selbstorganisierender Monoschichten, bestehend aus Mercaptoalkoholen und thiolmodifizierten DNA-Einzelsträngen, wird m

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York, William A. "Re-examining subfamily classifications for the alu family of repeated dna sequences." Master's thesis, University of Central Florida, 1994. http://digital.library.ucf.edu/cdm/ref/collection/RTD/id/24455.

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University of Central Florida College of Arts and Sciences Thesis<br>The primate Alu family of repetitive elements has been wedely characterized. This ubiquitous class of retroposons has been found to occupy some 5% of the human genome. This hetergenous group of Short Interspersed Nucleic acid Elements (SINEs) has been theorized to possess an identifiable subfamily structure between and within various taxonomic levels in promates. It has been postulated that humans possess up to 6 Alu sequences and found evidence supporting the amplification/fixation theory in 5 subfamilies. The research p

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Buchholz, Frank, Anja Nitzsche, Maciej Paszkowski-Rogacz, et al. "RAD21 Cooperates with Pluripotency Transcription Factors in the Maintenance of Embryonic Stem Cell Identity." Public Library of Science, 2011. https://tud.qucosa.de/id/qucosa%3A29134.

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For self-renewal, embryonic stem cells (ESCs) require the expression of specific transcription factors accompanied by a particular chromosome organization to maintain a balance between pluripotency and the capacity for rapid differentiation. However, how transcriptional regulation is linked to chromosome organization in ESCs is not well understood. Here we show that the cohesin component RAD21 exhibits a functional role in maintaining ESC identity through association with the pluripotency transcriptional network. ChIP-seq analyses of RAD21 reveal an ESC specific cohesin binding pattern that is

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Betiol, Julio Cesar. "Prevalência de HPV em tumores de cabeça e pescoço de São Paulo, Brasil." Universidade de São Paulo, 2014. http://www.teses.usp.br/teses/disponiveis/5/5155/tde-28012015-141538/.

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INTRODUÇÃO: O papilomavírus humano (HPV) encontra-se amplamente distribuído na população mundial. Apesar da grande maioria das infecções serem transientes, assintomáticas e passíveis de regressão espontânea, a infecção persistente por tipos de alto risco de HPV é necessária para o desenvolvimento de neoplasias intraepiteliais cervicais. Uma vez que apenas uma pequena parcela das infecções progride à lesões malignas após um longo período desde o diagnóstico inicial de lesões precursoras, tem-se iniciado a busca por fatores que possam influenciar na progressão ou na eliminação destas manifestaçõ

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Books on the topic "DNA Sequence Classification"

1

Petti, Cathy A. Interpretive criteria for identification of bacteria and fungi by DNA target sequencing: Approved guideline. Clinical and Laboratory Standards Institute, 2008.

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Dostal, Stefan. Concise guide to mycobacteria and their molecular differentiation. Ridom Press, 2003.

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Soltis, Pamela S., Douglas E. Soltis, and J. J. Doyle. Molecular Systematics of Plants II - DNA Sequencing. Springer, 1998.

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E, Soltis Douglas, Soltis Pamela S, and Doyle Jeff J, eds. Molecular systematics of plants II: DNA sequencing. Kluwer Academic Publishers, 1998.

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Erko, Stackebrandt, and Goodfellow M, eds. Nucleic acid techniques in bacterial systematics. Wiley, 1991.

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Grubisha, Lisa C. Systematics of the genus Rhizopogon inferred from nuclear ribosomal DNA large subunit and internal transcribed spacer sequences. 1998.

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Book chapters on the topic "DNA Sequence Classification"

1

Levy, Samuel, and Gary D. Stormo. "DNA sequence classification using DAWGs." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/3-540-63246-8_21.

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Lo Bosco, Giosué, and Mattia Antonino Di Gangi. "Deep Learning Architectures for DNA Sequence Classification." In Fuzzy Logic and Soft Computing Applications. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-52962-2_14.

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Rizzo, Riccardo, Antonino Fiannaca, Massimo La Rosa, and Alfonso Urso. "A Deep Learning Approach to DNA Sequence Classification." In Computational Intelligence Methods for Bioinformatics and Biostatistics. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-44332-4_10.

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Borman, Andrew M., and Elizabeth M. Johnson. "Sequence-based Identification and Classification of Fungi." In Trends in the systematics of bacteria and fungi. CABI, 2021. http://dx.doi.org/10.1079/9781789244984.0198.

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Abstract This book chapter describes the advantages and limitations of the ITS Region as a universal barcode for fungal identification. The ITS region offers several practical advantages as a universal fungal barcode region. The region encompasses segments that permit resolution at different taxonomic levels as it includes the highly conserved 5.8S rRNA gene, the moderately rapidly evolving ITS2 region and the rapidly evolving ITS1 region, flanked by the highly conserved SSU and LSU genes which permit design of PCR primers that are almost panfungal. Over the last two decades the sequence-based identification of fungi has certainly come of age. The ITS region is universally accepted as the primary fungal barcoding region owing to the high barcode gap with the locus for many groups of fungi. Since the species-resolution power of ITS is poor for certain groups of fungi, and higher-level taxonomic resolution is greater with proteincoding genes, the TEF1α locus has been proposed as the universal secondary barcode region. In addition, the historical problems surrounding the reliability of fungal DNA sequences in centralized repositories are slowly being resolved by the development of an increasing number of publicly accessible, curated databases.

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Dakhli, Abdesselem, Wajdi Bellil, and Chokri Ben Amar. "DNA Sequence Classification Using Power Spectrum and Wavelet Neural Network." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-52941-7_39.

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Dakhli, Abdesselem, Wajdi Bellil, and Chokri Ben Amar. "Wavelet Neural Network Initialization Using LTS for DNA Sequence Classification." In Advanced Concepts for Intelligent Vision Systems. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-48680-2_58.

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Eom, Jae-Hong, Seong-Bae Park, and Byoung-Tak Zhang. "Genetic Mining of DNA Sequence Structures for Effective Classification of the Risk Types of Human Papillomavirus (HPV)." In Neural Information Processing. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30499-9_208.

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Subbotin, Sergei A. "Phylogenetic analysis of DNA sequence data." In Techniques for work with plant and soil nematodes. CABI, 2021. http://dx.doi.org/10.1079/9781786391759.0265.

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Abstract The goal of phylogenetics is to construct relationships that are true representations of the evolutionary history of a group of organisms or genes. The history inferred from phylogenetic analysis is usually depicted as branching in tree-like diagrams or networks. In nematology, phylogenetic studies have been applied to resolve a wide range of questions dealing with improving classifications and testing evolution processes, such as co-evolution, biogeography and many others. There are several main steps involved in a phylogenetic study: (i) selection of ingroup and outgroup taxa for a study; (ii) selection of one or several gene fragments for a study; (iii) sample collection, obtaining PCR products and sequencing of gene fragments; (iv) visualization, editing raw sequence data and sequence assembling; (v) search for sequence similarity in a public database; (vi) making and editing multiple alignment of sequences; (vii) selecting appropriate DNA model for a dataset; (viii) phylogenetic reconstruction using minimum evolution, maximum parsimony, maximum likelihood and Bayesian inference; (ix) visualization of tree files and preparation of tree for a publication; and (x) sequence submission to a public database. Molecular phylogenetic study requires particularly careful planning because it is usually relatively expensive in terms of the cost in reagents and time.

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Subbotin, Sergei A. "Phylogenetic analysis of DNA sequence data." In Techniques for work with plant and soil nematodes. CABI, 2021. http://dx.doi.org/10.1079/9781786391759.0015.

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Abstract The goal of phylogenetics is to construct relationships that are true representations of the evolutionary history of a group of organisms or genes. The history inferred from phylogenetic analysis is usually depicted as branching in tree-like diagrams or networks. In nematology, phylogenetic studies have been applied to resolve a wide range of questions dealing with improving classifications and testing evolution processes, such as co-evolution, biogeography and many others. There are several main steps involved in a phylogenetic study: (i) selection of ingroup and outgroup taxa for a study; (ii) selection of one or several gene fragments for a study; (iii) sample collection, obtaining PCR products and sequencing of gene fragments; (iv) visualization, editing raw sequence data and sequence assembling; (v) search for sequence similarity in a public database; (vi) making and editing multiple alignment of sequences; (vii) selecting appropriate DNA model for a dataset; (viii) phylogenetic reconstruction using minimum evolution, maximum parsimony, maximum likelihood and Bayesian inference; (ix) visualization of tree files and preparation of tree for a publication; and (x) sequence submission to a public database. Molecular phylogenetic study requires particularly careful planning because it is usually relatively expensive in terms of the cost in reagents and time.

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Saada, Bacem, and Jing Zhang. "DNA Sequences Classification Using Data Analysis." In Transactions on Engineering Technologies. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0746-1_26.

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Conference papers on the topic "DNA Sequence Classification"

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Kaizhong, Jiang, Zheng Zhongtuan, Hong Yinping, and Li Lu. "Classification analysis of DNA sequences based on inverse sequence frequency." In 2012 5th International Conference on Biomedical Engineering and Informatics (BMEI). IEEE, 2012. http://dx.doi.org/10.1109/bmei.2012.6512964.

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McEachern, Andrew, and Daniel Ashlock. "Woven string kernels for DNA sequence classification." In 2013 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2013. http://dx.doi.org/10.1109/cec.2013.6557750.

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Zhou, Qingda, Qingshan Jiang, and Dan Wei. "A new method for classification in DNA sequence." In Education (ICCSE 2011). IEEE, 2011. http://dx.doi.org/10.1109/iccse.2011.6028621.

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Ma, Baoshan, Yisheng Zhu, and Yuzhen Chen. "An Improved Fourier Method for DNA Sequence Classification." In 2009 3rd International Conference on Bioinformatics and Biomedical Engineering (iCBBE). IEEE, 2009. http://dx.doi.org/10.1109/icbbe.2009.5162325.

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Kristensen, Terje, and Fabien Guillaume. "Different regimes for classification of DNA sequences." In 2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM). IEEE, 2015. http://dx.doi.org/10.1109/iccis.2015.7274558.

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Ounit, Rachid, Timothy Close, Steve Wanamaker, and Stefano Lonardi. "CLARK, accurate and efficient classification of DNA sequences." In BCB '14: ACM-BCB '14. ACM, 2014. http://dx.doi.org/10.1145/2649387.2662449.

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Wang, Lianhong, Jing Zhang, Xiaofeng Huang, and Gufeng Gong. "DNA Sequences Classification Based on Immune Evolution Network." In 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). IEEE, 2008. http://dx.doi.org/10.1109/fskd.2008.126.

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You, Wei, Kun Wang, Huixiao Li, Yang Jia, Xiaoqin Wu, and Yaning Du. "Classification of DNA Sequences Basing on the Dinucleotide Compositions." In 2009 Second International Symposium on Computational Intelligence and Design. IEEE, 2009. http://dx.doi.org/10.1109/iscid.2009.244.

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Bercher, J. F., P. Jardin, and B. Duriez. "Bayesian classification for promoter prediction in human DNA sequences." In Bayesian Inference and Maximum Entropy Methods In Science and Engineering. AIP, 2006. http://dx.doi.org/10.1063/1.2423280.

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Raghavendra, B. S., and A. S. Bopardikar. "Identification of CpG islands in DNA sequences using supervised classification." In 2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW). IEEE, 2011. http://dx.doi.org/10.1109/bibmw.2011.6112519.

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