Literatura científica selecionada sobre o tema "Sequence similarity analysis"
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Artigos de revistas sobre o assunto "Sequence similarity analysis"
Wei, Dan, Qingshan Jiang e Sheng Li. "A New Approach for DNA Sequence Similarity Analysis based on Triplets of Nucleic Acid Bases". International Journal of Nanotechnology and Molecular Computation 2, n.º 4 (outubro de 2010): 1–11. http://dx.doi.org/10.4018/978-1-60960-064-8.ch006.
Texto completo da fonteLi, Hongliang, e Bin Liu. "BioSeq-Diabolo: Biological sequence similarity analysis using Diabolo". PLOS Computational Biology 19, n.º 6 (20 de junho de 2023): e1011214. http://dx.doi.org/10.1371/journal.pcbi.1011214.
Texto completo da fontede Oliveira Martins, Leonardo, Alison E. Mather e Andrew J. Page. "Scalable neighbour search and alignment with uvaia". PeerJ 12 (6 de março de 2024): e16890. http://dx.doi.org/10.7717/peerj.16890.
Texto completo da fonteVan Reenen, C. A., W. H. Van Zyl e L. M. T. Dicks. "Expression of the Immunity Protein of Plantaricin 423, Produced by Lactobacillus plantarum 423, and Analysis of the Plasmid Encoding the Bacteriocin". Applied and Environmental Microbiology 72, n.º 12 (20 de outubro de 2006): 7644–51. http://dx.doi.org/10.1128/aem.01428-06.
Texto completo da fontePacheco, Richard C., Jonas Moraes-Filho, Arlei Marcili, Leonardo J. Richtzenhain, Matias P. J. Szabó, Márcia H. B. Catroxo, Donald H. Bouyer e Marcelo B. Labruna. "Rickettsia monteiroi sp. nov., Infecting the Tick Amblyomma incisum in Brazil". Applied and Environmental Microbiology 77, n.º 15 (17 de junho de 2011): 5207–11. http://dx.doi.org/10.1128/aem.05166-11.
Texto completo da fonteSmallwood, M., J. N. Keen e D. J. Bowles. "Purification and partial sequence analysis of plant annexins". Biochemical Journal 270, n.º 1 (15 de agosto de 1990): 157–61. http://dx.doi.org/10.1042/bj2700157.
Texto completo da fonteGyörgyey, János, Danièle Vaubert, José I. Jiménez-Zurdo, Celine Charon, Liliane Troussard, Ádám Kondorosi e Éva Kondorosi. "Analysis of Medicago truncatula Nodule Expressed Sequence Tags". Molecular Plant-Microbe Interactions® 13, n.º 1 (janeiro de 2000): 62–71. http://dx.doi.org/10.1094/mpmi.2000.13.1.62.
Texto completo da fonteXu, Fuyu, e Kate Beard. "A Unifying Framework for Analysis of Spatial-Temporal Event Sequence Similarity and Its Applications". ISPRS International Journal of Geo-Information 10, n.º 9 (9 de setembro de 2021): 594. http://dx.doi.org/10.3390/ijgi10090594.
Texto completo da fonteNikhila, K. S., e Vrinda V. Nair. "Protein Sequence Similarity Analysis Using Computational Techniques". Materials Today: Proceedings 5, n.º 1 (2018): 724–31. http://dx.doi.org/10.1016/j.matpr.2017.11.139.
Texto completo da fonteHark Gan, Hin, Rebecca A. Perlow, Sharmili Roy, Joy Ko, Min Wu, Jing Huang, Shixiang Yan et al. "Analysis of Protein Sequence/Structure Similarity Relationships". Biophysical Journal 83, n.º 5 (novembro de 2002): 2781–91. http://dx.doi.org/10.1016/s0006-3495(02)75287-9.
Texto completo da fonteTeses / dissertações sobre o assunto "Sequence similarity analysis"
Joseph, Arokiya Louis Monica. "Sequence Similarity Search portal". CSUSB ScholarWorks, 2007. https://scholarworks.lib.csusb.edu/etd-project/3124.
Texto completo da fonteChen, Zhuo. "Smart Sequence Similarity Search (S⁴) system". CSUSB ScholarWorks, 2004. https://scholarworks.lib.csusb.edu/etd-project/2458.
Texto completo da fonteMendoza, Leon Jesus Alexis. "Analysis of DNA sequence similarity within organisms causing New World leishmaniasis". Thesis, University of Cambridge, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.386206.
Texto completo da fonteBatra, Sushil Baker Erich J. Lee Myeongwoo. "Identification of phenotypes in Caenorabhditis elegans on the basis of sequence similarity". Waco, Tex. : Baylor University, 2009. http://hdl.handle.net/2104/5325.
Texto completo da fonteOzturk, Ozgur. "Feature extraction and similarity-based analysis for proteome and genome databases". The Ohio State University, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=osu1190138805.
Texto completo da fonteMitas̃iūnaite, Ieva. "Mining string data under similarity and soft-frequency constraints : application to promoter sequence analysis". Lyon, INSA, 2009. http://theses.insa-lyon.fr/publication/2009ISAL0036/these.pdf.
Texto completo da fonteAn inductive database is a database that contains not only data but also patterns. Inductive databases are designed to support the KDD process. Recent advances in inductive databases research have given rise to a generic solvers capable of solving inductive queries that are arbitrary Boolean combinations of anti-monotonic and monotonic constraints. They are designed to mine different types of pattern (i. E. , patterns from different pattern languages). An instance of such a generic solver exists that is capable of mining string patterns from string data sets. In our main application, promoter sequence analysis, there is a requirement to handle fault-tolerance, as the data intrinsically contains errors, and the phenomenon we are trying to capture is fundamentally degenerate. Our research contribution to fault-tolerant pattern extraction in string data sets is the use of a generic solver, based on a non-trivial formalisation of fault-tolerant pattern extraction as a constraint-based mining task. We identified the stages in the process of the extraction of such patterns where state-of-art strategies can be applied to prune the search space. We then developed a fault-tolerant pattern match function InsDels that generic constraint solving strategies can soundly tackle. We also focused on making local patterns actionable. The bottleneck of most local pattern extraction methods is the burden of spurious patterns. As the analysis of patterns by the application domain experts is time consuming, we cannot afford to present patterns without any objective clue about their relevancy. Therefore we have developed two methods of computing the expected number of patterns extracted in random data sets. If the number of extracted patterns is strongly different from the expected number from random data sets, one can then state that the results exhibits local associations that are a priori relevant because they are unexpected. Among others applications, we have applied our approach to support the discovery of new motifs in gene promoter sequences with promising results
Sacan, Ahmet. "Similarity Search And Analysis Of Protein Sequences And Structures: A Residue Contacts Based Approach". Phd thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/12609754/index.pdf.
Texto completo da fonteWessel, Jennifer. "Human genetic-epidemiologic association analysis via allelic composition and DNA sequence similarity methods applications to blood-based gene expression biomarkers of disease /". Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2006. http://wwwlib.umi.com/cr/ucsd/fullcit?p3237548.
Texto completo da fonteTitle from first page of PDF file (viewed December 12, 2006). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references.
Wang, Danling. "Multifractal characterisation and analysis of complex networks". Thesis, Queensland University of Technology, 2011. https://eprints.qut.edu.au/48176/1/Danling_Wang_Thesis.pdf.
Texto completo da fonteYan, Yiqing. "Scalable and accurate algorithms for computational genomics and dna-based digital storage". Electronic Thesis or Diss., Sorbonne université, 2023. http://www.theses.fr/2023SORUS078.
Texto completo da fonteCost reduction and throughput improvement in sequencing technology have resulted in new advances in applications such as precision medicine and DNA-based storage. However, the sequenced result contains errors. To measure the similarity between the sequenced result and reference, edit distance is preferred in practice over Hamming distance due to the indels. The primitive edit distance calculation is quadratic complex. Therefore, sequence similarity analysis is computationally intensive. In this thesis, we introduce two accurate and scalable sequence similarity analysis algorithms, i) Accel-Align, a fast sequence mapper and aligner based on the seed–embed–extend methodology, and ii) Motif-Search, an efficient structure-aware algorithm to recover the information encoded by the composite motifs from the DNA archive. Then, we use Accel-Align as an efficient tool to study the random access design in DNA-based storage
Livros sobre o assunto "Sequence similarity analysis"
Bilański, Piotr. Trypodendron laeve Eggers w Polsce na tle wybranych aspektów morfologicznych i genetycznych drwalników (Trypodendron spp., Coleoptera, Curculionidae, Scolytinae). Publishing House of the University of Agriculture in Krakow, 2019. http://dx.doi.org/10.15576/978-83-66602-38-0.
Texto completo da fonteCapítulos de livros sobre o assunto "Sequence similarity analysis"
States, David J., e Mark S. Boguski. "Similarity and Homology". In Sequence Analysis Primer, 89–157. London: Palgrave Macmillan UK, 1991. http://dx.doi.org/10.1007/978-1-349-21355-9_3.
Texto completo da fonteAdjeroh, D. A., I. King e M. C. Lee. "Video sequence similarity matching". In Multimedia Information Analysis and Retrieval, 80–95. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0016490.
Texto completo da fonteYap, Tieng K., Ophir Frieder e Robert L. Martino. "Multiprocessor Sequence Similarity Searching". In High Performance Computational Methods for Biological Sequence Analysis, 143–57. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4613-1391-5_6.
Texto completo da fonteRyšavý, Petr, e Filip Železný. "Estimating Sequence Similarity from Contig Sets". In Advances in Intelligent Data Analysis XVI, 272–83. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68765-0_23.
Texto completo da fonteSubbotin, Sergei A. "Phylogenetic analysis of DNA sequence data." In Techniques for work with plant and soil nematodes, 265–82. Wallingford: CABI, 2021. http://dx.doi.org/10.1079/9781786391759.0265.
Texto completo da fonteSubbotin, Sergei A. "Phylogenetic analysis of DNA sequence data." In Techniques for work with plant and soil nematodes, 265–82. Wallingford: CABI, 2021. http://dx.doi.org/10.1079/9781786391759.0015.
Texto completo da fonteUng, Huy Quang, Cuong Tuan Nguyen, Hung Tuan Nguyen e Masaki Nakagawa. "GSSF: A Generative Sequence Similarity Function Based on a Seq2Seq Model for Clustering Online Handwritten Mathematical Answers". In Document Analysis and Recognition – ICDAR 2021, 145–59. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86331-9_10.
Texto completo da fonteXie, Bo, e Long Chen. "Automatic Scoring Model of Subjective Questions Based Text Similarity Fusion Model". In Proceeding of 2021 International Conference on Wireless Communications, Networking and Applications, 586–99. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2456-9_60.
Texto completo da fonteBonnici, Vincenzo, Andrea Cracco e Giuditta Franco. "A k-mer Based Sequence Similarity for Pangenomic Analyses". In Machine Learning, Optimization, and Data Science, 31–44. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-95470-3_3.
Texto completo da fonteYang, Wenlu, Xiongjun Pi e Liqing Zhang. "Similarity Analysis of DNA Sequences Based on the Relative Entropy". In Lecture Notes in Computer Science, 1035–38. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11539087_137.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Sequence similarity analysis"
Yusen Zhang e Xiangtian Yu. "Analysis of protein sequence similarity". In 2010 IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA). IEEE, 2010. http://dx.doi.org/10.1109/bicta.2010.5645085.
Texto completo da fonteKetterlin, Alain, e Pierre Ganarski. "Sequence Similarity and Multi-Date Image Segmentation". In 2007 International Workshop on the Analysis of Multi-temporal Remote Sensing Images. IEEE, 2007. http://dx.doi.org/10.1109/multitemp.2007.4293034.
Texto completo da fonteLei, H., e Venu Govindaraju. "Similarity-driven sequence classification based on support vector machines". In Eighth International Conference on Document Analysis and Recognition (ICDAR'05). IEEE, 2005. http://dx.doi.org/10.1109/icdar.2005.217.
Texto completo da fonteYang, Lina, Yuan Yan Tang, Yulong Wang, Huiwu Luo, Jianjia Pan, Haoliang Yuan, Xianwei Zheng, Chunli Li e Ting Shu. "Similarity analysis based on sparse representation for protein sequence comparison". In 2015 IEEE 2nd International Conference on Cybernetics (CYBCONF). IEEE, 2015. http://dx.doi.org/10.1109/cybconf.2015.7175964.
Texto completo da fonteKong, Fen, Xu-ying Nan, Ping-an He, Qi Dai e Yu-hua Yao. "A sequence-segmented method applied to the similarity analysis of proteins". In 2012 IEEE 6th International Conference on Systems Biology (ISB). IEEE, 2012. http://dx.doi.org/10.1109/isb.2012.6314157.
Texto completo da fonteHu, Jun, Hongxia Zhao, Xueyou Liang e Dan Chen. "The analysis of similarity for promoter sequence structures in yeast genes". In 2012 5th International Conference on Biomedical Engineering and Informatics (BMEI). IEEE, 2012. http://dx.doi.org/10.1109/bmei.2012.6513091.
Texto completo da fonteDang, Xiaocui, Lina Yang, Yuan Yan Tang, Pu Wei e Hailong Su. "Nd5 Protein Sequence Similarity Analysis Based On Discrete Wavelet Transform And Fractal Dimension". In 2019 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR). IEEE, 2019. http://dx.doi.org/10.1109/icwapr48189.2019.8946464.
Texto completo da fonteBai, Fenglan, e Jun Xu. "Sequence Similarity Analysis of Buthus Martensii Neurotoxin Genes Based on Structure Matrix". In 2012 Fourth International Conference on Computational and Information Sciences (ICCIS). IEEE, 2012. http://dx.doi.org/10.1109/iccis.2012.277.
Texto completo da fonteZhang, Hainan, Yanyan Lan, Jiafeng Guo, Jun Xu e Xueqi Cheng. "Reinforcing Coherence for Sequence to Sequence Model in Dialogue Generation". In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/635.
Texto completo da fonteGupta, Manoj Kumar, Rajdeep Niyogi e Manoj Misra. "A framework for alignment-free methods to perform similarity analysis of biological sequence". In 2013 Sixth International Conference on Contemporary Computing (IC3). IEEE, 2013. http://dx.doi.org/10.1109/ic3.2013.6612216.
Texto completo da fonteRelatórios de organizações sobre o assunto "Sequence similarity analysis"
Gelb, Jr., Jack, Yoram Weisman, Brian Ladman e Rosie Meir. Identification of Avian Infectious Brochitis Virus Variant Serotypes and Subtypes by PCR Product Cycle Sequencing for the Rational Selection of Effective Vaccines. United States Department of Agriculture, dezembro de 2003. http://dx.doi.org/10.32747/2003.7586470.bard.
Texto completo da fonteDickman, Martin B., e Oded Yarden. Role of Phosphorylation in Fungal Spore Germination. United States Department of Agriculture, agosto de 1993. http://dx.doi.org/10.32747/1993.7568761.bard.
Texto completo da fonteCohen, Yuval, Christopher A. Cullis e Uri Lavi. Molecular Analyses of Soma-clonal Variation in Date Palm and Banana for Early Identification and Control of Off-types Generation. United States Department of Agriculture, outubro de 2010. http://dx.doi.org/10.32747/2010.7592124.bard.
Texto completo da fonteFridman, Eyal, e Eran Pichersky. Tomato Natural Insecticides: Elucidation of the Complex Pathway of Methylketone Biosynthesis. United States Department of Agriculture, dezembro de 2009. http://dx.doi.org/10.32747/2009.7696543.bard.
Texto completo da fonteMichelmore, Richard, Eviatar Nevo, Abraham Korol e Tzion Fahima. Genetic Diversity at Resistance Gene Clusters in Wild Populations of Lactuca. United States Department of Agriculture, fevereiro de 2000. http://dx.doi.org/10.32747/2000.7573075.bard.
Texto completo da fonteUeti, Massaro Wilson, e Monica Leszkowicz Mazuz. Identification, characterization and testing of geographically conserved Babesia bovis vaccine antigen candidates. Israel: United States-Israel Binational Agricultural Research and Development Fund, 2022. http://dx.doi.org/10.32747/2022.8134143.bard.
Texto completo da fonteLevisohn, Sharon, Maricarmen Garcia, David Yogev e Stanley Kleven. Targeted Molecular Typing of Pathogenic Avian Mycoplasmas. United States Department of Agriculture, janeiro de 2006. http://dx.doi.org/10.32747/2006.7695853.bard.
Texto completo da fonteParan, Ilan, e Allen Van Deynze. Regulation of pepper fruit color, chloroplasts development and their importance in fruit quality. United States Department of Agriculture, janeiro de 2014. http://dx.doi.org/10.32747/2014.7598173.bard.
Texto completo da fonteOr, Etti, David Galbraith e Anne Fennell. Exploring mechanisms involved in grape bud dormancy: Large-scale analysis of expression reprogramming following controlled dormancy induction and dormancy release. United States Department of Agriculture, dezembro de 2002. http://dx.doi.org/10.32747/2002.7587232.bard.
Texto completo da fonteRafaeli, Ada, e Russell Jurenka. Molecular Characterization of PBAN G-protein Coupled Receptors in Moth Pest Species: Design of Antagonists. United States Department of Agriculture, dezembro de 2012. http://dx.doi.org/10.32747/2012.7593390.bard.
Texto completo da fonte